Overview

Dataset statistics

Number of variables124
Number of observations1700
Missing cells15974
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory992.1 B

Variable types

Numeric16
Categorical108

Warnings

ID is highly correlated with LET_ISHigh correlation
AGE is highly correlated with KFK_BLOODHigh correlation
SEX is highly correlated with KFK_BLOODHigh correlation
STENOK_AN is highly correlated with FK_STENOK and 1 other fieldsHigh correlation
FK_STENOK is highly correlated with STENOK_AN and 1 other fieldsHigh correlation
GB is highly correlated with DLIT_AGHigh correlation
DLIT_AG is highly correlated with GBHigh correlation
ZSN_A is highly correlated with KFK_BLOODHigh correlation
nr_04 is highly correlated with ritm_ecg_p_02 and 1 other fieldsHigh correlation
endocr_02 is highly correlated with KFK_BLOODHigh correlation
S_AD_KBRIG is highly correlated with D_AD_KBRIG and 2 other fieldsHigh correlation
D_AD_KBRIG is highly correlated with S_AD_KBRIG and 3 other fieldsHigh correlation
S_AD_ORIT is highly correlated with S_AD_KBRIG and 3 other fieldsHigh correlation
D_AD_ORIT is highly correlated with S_AD_KBRIG and 4 other fieldsHigh correlation
K_SH_POST is highly correlated with D_AD_KBRIG and 1 other fieldsHigh correlation
MP_TP_POST is highly correlated with ritm_ecg_p_02 and 1 other fieldsHigh correlation
SVT_POST is highly correlated with n_r_ecg_p_08High correlation
ant_im is highly correlated with lat_im and 2 other fieldsHigh correlation
lat_im is highly correlated with ant_imHigh correlation
inf_im is highly correlated with ant_im and 1 other fieldsHigh correlation
ritm_ecg_p_01 is highly correlated with ritm_ecg_p_07 and 1 other fieldsHigh correlation
ritm_ecg_p_02 is highly correlated with nr_04 and 4 other fieldsHigh correlation
ritm_ecg_p_04 is highly correlated with n_p_ecg_p_06High correlation
ritm_ecg_p_07 is highly correlated with ritm_ecg_p_01High correlation
n_r_ecg_p_05 is highly correlated with MP_TP_POST and 1 other fieldsHigh correlation
n_r_ecg_p_06 is highly correlated with nr_04 and 2 other fieldsHigh correlation
n_r_ecg_p_08 is highly correlated with SVT_POSTHigh correlation
n_p_ecg_p_06 is highly correlated with ritm_ecg_p_04High correlation
n_p_ecg_p_10 is highly correlated with KFK_BLOODHigh correlation
GIPO_K is highly correlated with K_BLOODHigh correlation
K_BLOOD is highly correlated with GIPO_KHigh correlation
ALT_BLOOD is highly correlated with AST_BLOODHigh correlation
AST_BLOOD is highly correlated with ALT_BLOODHigh correlation
KFK_BLOOD is highly correlated with AGE and 22 other fieldsHigh correlation
L_BLOOD is highly correlated with KFK_BLOODHigh correlation
ROE is highly correlated with KFK_BLOODHigh correlation
TIME_B_S is highly correlated with KFK_BLOODHigh correlation
R_AB_1_n is highly correlated with NA_R_1_nHigh correlation
R_AB_2_n is highly correlated with NA_R_2_nHigh correlation
R_AB_3_n is highly correlated with NA_R_3_nHigh correlation
NA_KB is highly correlated with KFK_BLOODHigh correlation
LID_KB is highly correlated with KFK_BLOODHigh correlation
NA_R_1_n is highly correlated with KFK_BLOOD and 1 other fieldsHigh correlation
NA_R_2_n is highly correlated with R_AB_2_nHigh correlation
NA_R_3_n is highly correlated with R_AB_3_nHigh correlation
NOT_NA_2_n is highly correlated with NOT_NA_3_nHigh correlation
NOT_NA_3_n is highly correlated with NOT_NA_2_nHigh correlation
LID_S_n is highly correlated with KFK_BLOODHigh correlation
GEPAR_S_n is highly correlated with KFK_BLOODHigh correlation
FIBR_JELUD is highly correlated with KFK_BLOODHigh correlation
LET_IS is highly correlated with IDHigh correlation
ID is highly correlated with LET_ISHigh correlation
AGE is highly correlated with KFK_BLOODHigh correlation
SEX is highly correlated with KFK_BLOODHigh correlation
STENOK_AN is highly correlated with FK_STENOK and 1 other fieldsHigh correlation
FK_STENOK is highly correlated with STENOK_AN and 1 other fieldsHigh correlation
GB is highly correlated with DLIT_AGHigh correlation
DLIT_AG is highly correlated with GBHigh correlation
ZSN_A is highly correlated with KFK_BLOODHigh correlation
nr_04 is highly correlated with ritm_ecg_p_02 and 1 other fieldsHigh correlation
endocr_02 is highly correlated with KFK_BLOODHigh correlation
S_AD_KBRIG is highly correlated with D_AD_KBRIG and 2 other fieldsHigh correlation
D_AD_KBRIG is highly correlated with S_AD_KBRIGHigh correlation
S_AD_ORIT is highly correlated with S_AD_KBRIG and 2 other fieldsHigh correlation
D_AD_ORIT is highly correlated with S_AD_KBRIG and 2 other fieldsHigh correlation
MP_TP_POST is highly correlated with ritm_ecg_p_02 and 1 other fieldsHigh correlation
SVT_POST is highly correlated with n_r_ecg_p_08High correlation
ant_im is highly correlated with lat_im and 1 other fieldsHigh correlation
lat_im is highly correlated with ant_im and 2 other fieldsHigh correlation
inf_im is highly correlated with ant_im and 2 other fieldsHigh correlation
ritm_ecg_p_01 is highly correlated with ritm_ecg_p_07 and 1 other fieldsHigh correlation
ritm_ecg_p_02 is highly correlated with nr_04 and 4 other fieldsHigh correlation
ritm_ecg_p_04 is highly correlated with n_p_ecg_p_06High correlation
ritm_ecg_p_07 is highly correlated with ritm_ecg_p_01High correlation
n_r_ecg_p_05 is highly correlated with MP_TP_POST and 1 other fieldsHigh correlation
n_r_ecg_p_06 is highly correlated with nr_04 and 2 other fieldsHigh correlation
n_r_ecg_p_08 is highly correlated with SVT_POSTHigh correlation
n_p_ecg_p_06 is highly correlated with ritm_ecg_p_04High correlation
n_p_ecg_p_10 is highly correlated with KFK_BLOODHigh correlation
GIPO_K is highly correlated with K_BLOODHigh correlation
K_BLOOD is highly correlated with GIPO_KHigh correlation
NA_BLOOD is highly correlated with KFK_BLOODHigh correlation
ALT_BLOOD is highly correlated with AST_BLOOD and 1 other fieldsHigh correlation
AST_BLOOD is highly correlated with ALT_BLOOD and 1 other fieldsHigh correlation
KFK_BLOOD is highly correlated with AGE and 24 other fieldsHigh correlation
TIME_B_S is highly correlated with KFK_BLOODHigh correlation
R_AB_1_n is highly correlated with NA_R_1_nHigh correlation
R_AB_2_n is highly correlated with NA_R_2_nHigh correlation
R_AB_3_n is highly correlated with NA_R_3_nHigh correlation
NA_KB is highly correlated with KFK_BLOODHigh correlation
LID_KB is highly correlated with KFK_BLOODHigh correlation
NA_R_1_n is highly correlated with KFK_BLOOD and 1 other fieldsHigh correlation
NA_R_2_n is highly correlated with R_AB_2_nHigh correlation
NA_R_3_n is highly correlated with R_AB_3_nHigh correlation
GEPAR_S_n is highly correlated with KFK_BLOODHigh correlation
ASP_S_n is highly correlated with KFK_BLOODHigh correlation
FIBR_JELUD is highly correlated with KFK_BLOODHigh correlation
REC_IM is highly correlated with KFK_BLOODHigh correlation
LET_IS is highly correlated with IDHigh correlation
ID is highly correlated with LET_ISHigh correlation
AGE is highly correlated with KFK_BLOODHigh correlation
SEX is highly correlated with KFK_BLOODHigh correlation
STENOK_AN is highly correlated with FK_STENOK and 1 other fieldsHigh correlation
FK_STENOK is highly correlated with STENOK_AN and 1 other fieldsHigh correlation
GB is highly correlated with DLIT_AGHigh correlation
DLIT_AG is highly correlated with GBHigh correlation
ZSN_A is highly correlated with KFK_BLOODHigh correlation
nr_04 is highly correlated with ritm_ecg_p_02 and 1 other fieldsHigh correlation
endocr_02 is highly correlated with KFK_BLOODHigh correlation
S_AD_KBRIG is highly correlated with D_AD_KBRIGHigh correlation
D_AD_KBRIG is highly correlated with S_AD_KBRIGHigh correlation
S_AD_ORIT is highly correlated with D_AD_ORIT and 1 other fieldsHigh correlation
D_AD_ORIT is highly correlated with S_AD_ORIT and 1 other fieldsHigh correlation
MP_TP_POST is highly correlated with ritm_ecg_p_02 and 1 other fieldsHigh correlation
SVT_POST is highly correlated with n_r_ecg_p_08High correlation
ant_im is highly correlated with lat_im and 1 other fieldsHigh correlation
lat_im is highly correlated with ant_im and 1 other fieldsHigh correlation
inf_im is highly correlated with ant_im and 1 other fieldsHigh correlation
ritm_ecg_p_01 is highly correlated with ritm_ecg_p_07 and 1 other fieldsHigh correlation
ritm_ecg_p_02 is highly correlated with nr_04 and 4 other fieldsHigh correlation
ritm_ecg_p_04 is highly correlated with n_p_ecg_p_06High correlation
ritm_ecg_p_07 is highly correlated with ritm_ecg_p_01High correlation
n_r_ecg_p_05 is highly correlated with MP_TP_POST and 1 other fieldsHigh correlation
n_r_ecg_p_06 is highly correlated with nr_04 and 2 other fieldsHigh correlation
n_r_ecg_p_08 is highly correlated with SVT_POSTHigh correlation
n_p_ecg_p_06 is highly correlated with ritm_ecg_p_04High correlation
n_p_ecg_p_10 is highly correlated with KFK_BLOODHigh correlation
GIPO_K is highly correlated with K_BLOODHigh correlation
K_BLOOD is highly correlated with GIPO_KHigh correlation
KFK_BLOOD is highly correlated with AGE and 21 other fieldsHigh correlation
TIME_B_S is highly correlated with KFK_BLOODHigh correlation
R_AB_2_n is highly correlated with NA_R_2_nHigh correlation
R_AB_3_n is highly correlated with NA_R_3_nHigh correlation
NA_KB is highly correlated with KFK_BLOODHigh correlation
LID_KB is highly correlated with KFK_BLOODHigh correlation
NA_R_1_n is highly correlated with KFK_BLOODHigh correlation
NA_R_2_n is highly correlated with R_AB_2_nHigh correlation
NA_R_3_n is highly correlated with R_AB_3_nHigh correlation
GEPAR_S_n is highly correlated with KFK_BLOODHigh correlation
ASP_S_n is highly correlated with KFK_BLOODHigh correlation
FIBR_JELUD is highly correlated with KFK_BLOODHigh correlation
REC_IM is highly correlated with KFK_BLOODHigh correlation
LET_IS is highly correlated with IDHigh correlation
n_p_ecg_p_06 is highly correlated with ritm_ecg_p_04High correlation
NA_R_3_n is highly correlated with R_AB_3_nHigh correlation
S_AD_KBRIG is highly correlated with S_AD_ORIT and 3 other fieldsHigh correlation
endocr_02 is highly correlated with KFK_BLOODHigh correlation
ritm_ecg_p_01 is highly correlated with ritm_ecg_p_02 and 2 other fieldsHigh correlation
n_p_ecg_p_10 is highly correlated with KFK_BLOODHigh correlation
ritm_ecg_p_06 is highly correlated with n_r_ecg_p_10 and 8 other fieldsHigh correlation
LID_S_n is highly correlated with KFK_BLOODHigh correlation
ritm_ecg_p_02 is highly correlated with ritm_ecg_p_01 and 5 other fieldsHigh correlation
n_r_ecg_p_10 is highly correlated with ritm_ecg_p_06 and 2 other fieldsHigh correlation
ritm_ecg_p_04 is highly correlated with n_p_ecg_p_06High correlation
FIBR_JELUD is highly correlated with KFK_BLOODHigh correlation
ant_im is highly correlated with post_im and 3 other fieldsHigh correlation
n_r_ecg_p_05 is highly correlated with ritm_ecg_p_02 and 1 other fieldsHigh correlation
zab_leg_02 is highly correlated with KFK_BLOODHigh correlation
NA_R_1_n is highly correlated with KFK_BLOODHigh correlation
S_AD_ORIT is highly correlated with S_AD_KBRIG and 4 other fieldsHigh correlation
fibr_ter_08 is highly correlated with ritm_ecg_p_06 and 3 other fieldsHigh correlation
nr_07 is highly correlated with ritm_ecg_p_06 and 8 other fieldsHigh correlation
ritm_ecg_p_07 is highly correlated with ritm_ecg_p_01High correlation
GIPER_NA is highly correlated with NA_BLOODHigh correlation
R_AB_2_n is highly correlated with fibr_ter_06 and 4 other fieldsHigh correlation
SVT_POST is highly correlated with n_r_ecg_p_08High correlation
IBS_POST is highly correlated with KFK_BLOOD and 2 other fieldsHigh correlation
SEX is highly correlated with KFK_BLOOD and 1 other fieldsHigh correlation
post_im is highly correlated with ant_imHigh correlation
DLIT_AG is highly correlated with KFK_BLOOD and 1 other fieldsHigh correlation
fibr_ter_06 is highly correlated with R_AB_2_n and 1 other fieldsHigh correlation
IBS_NASL is highly correlated with S_AD_ORITHigh correlation
KFK_BLOOD is highly correlated with endocr_02 and 41 other fieldsHigh correlation
GIPO_K is highly correlated with K_BLOODHigh correlation
INF_ANAM is highly correlated with KFK_BLOODHigh correlation
nr_01 is highly correlated with ritm_ecg_p_06High correlation
GB is highly correlated with DLIT_AGHigh correlation
FK_STENOK is highly correlated with IBS_POST and 2 other fieldsHigh correlation
TIME_B_S is highly correlated with KFK_BLOODHigh correlation
n_r_ecg_p_09 is highly correlated with ritm_ecg_p_06 and 3 other fieldsHigh correlation
GEPAR_S_n is highly correlated with KFK_BLOODHigh correlation
NA_R_2_n is highly correlated with R_AB_2_n and 3 other fieldsHigh correlation
AST_BLOOD is highly correlated with KFK_BLOOD and 1 other fieldsHigh correlation
NOT_NA_3_n is highly correlated with NOT_NA_2_nHigh correlation
ALT_BLOOD is highly correlated with KFK_BLOOD and 1 other fieldsHigh correlation
L_BLOOD is highly correlated with KFK_BLOODHigh correlation
RAZRIV is highly correlated with LET_ISHigh correlation
NOT_NA_2_n is highly correlated with R_AB_2_n and 1 other fieldsHigh correlation
K_BLOOD is highly correlated with KFK_BLOOD and 1 other fieldsHigh correlation
inf_im is highly correlated with ant_im and 2 other fieldsHigh correlation
np_07 is highly correlated with n_r_ecg_p_10 and 7 other fieldsHigh correlation
np_01 is highly correlated with ritm_ecg_p_06 and 2 other fieldsHigh correlation
R_AB_3_n is highly correlated with NA_R_3_n and 3 other fieldsHigh correlation
ID is highly correlated with KFK_BLOOD and 1 other fieldsHigh correlation
NA_BLOOD is highly correlated with GIPER_NA and 1 other fieldsHigh correlation
nr_04 is highly correlated with ritm_ecg_p_02 and 2 other fieldsHigh correlation
endocr_01 is highly correlated with KFK_BLOODHigh correlation
LET_IS is highly correlated with RAZRIV and 2 other fieldsHigh correlation
n_r_ecg_p_08 is highly correlated with SVT_POSTHigh correlation
NITR_S is highly correlated with KFK_BLOODHigh correlation
n_p_ecg_p_01 is highly correlated with ritm_ecg_p_06 and 2 other fieldsHigh correlation
D_AD_KBRIG is highly correlated with S_AD_KBRIG and 3 other fieldsHigh correlation
ROE is highly correlated with KFK_BLOODHigh correlation
n_p_ecg_p_05 is highly correlated with ritm_ecg_p_06 and 2 other fieldsHigh correlation
lat_im is highly correlated with ant_im and 2 other fieldsHigh correlation
ZSN is highly correlated with KFK_BLOODHigh correlation
zab_leg_01 is highly correlated with KFK_BLOODHigh correlation
ANT_CA_S_n is highly correlated with KFK_BLOODHigh correlation
REC_IM is highly correlated with KFK_BLOODHigh correlation
np_09 is highly correlated with ritm_ecg_p_06 and 2 other fieldsHigh correlation
OTEK_LANC is highly correlated with KFK_BLOODHigh correlation
K_SH_POST is highly correlated with S_AD_KBRIG and 4 other fieldsHigh correlation
D_AD_ORIT is highly correlated with S_AD_KBRIG and 3 other fieldsHigh correlation
R_AB_1_n is highly correlated with KFK_BLOODHigh correlation
STENOK_AN is highly correlated with IBS_POST and 2 other fieldsHigh correlation
n_r_ecg_p_06 is highly correlated with ritm_ecg_p_02 and 3 other fieldsHigh correlation
ZSN_A is highly correlated with KFK_BLOODHigh correlation
AGE is highly correlated with SEX and 1 other fieldsHigh correlation
ASP_S_n is highly correlated with KFK_BLOODHigh correlation
MP_TP_POST is highly correlated with ritm_ecg_p_02 and 4 other fieldsHigh correlation
STENOK_AN has 106 (6.2%) missing values Missing
FK_STENOK has 73 (4.3%) missing values Missing
IBS_POST has 51 (3.0%) missing values Missing
IBS_NASL has 1628 (95.8%) missing values Missing
DLIT_AG has 248 (14.6%) missing values Missing
ZSN_A has 54 (3.2%) missing values Missing
nr_11 has 21 (1.2%) missing values Missing
nr_01 has 21 (1.2%) missing values Missing
nr_02 has 21 (1.2%) missing values Missing
nr_03 has 21 (1.2%) missing values Missing
nr_04 has 21 (1.2%) missing values Missing
nr_07 has 21 (1.2%) missing values Missing
nr_08 has 21 (1.2%) missing values Missing
np_01 has 18 (1.1%) missing values Missing
np_04 has 18 (1.1%) missing values Missing
np_05 has 18 (1.1%) missing values Missing
np_07 has 18 (1.1%) missing values Missing
np_08 has 18 (1.1%) missing values Missing
np_09 has 18 (1.1%) missing values Missing
np_10 has 18 (1.1%) missing values Missing
S_AD_KBRIG has 1076 (63.3%) missing values Missing
D_AD_KBRIG has 1076 (63.3%) missing values Missing
S_AD_ORIT has 267 (15.7%) missing values Missing
D_AD_ORIT has 267 (15.7%) missing values Missing
ant_im has 83 (4.9%) missing values Missing
lat_im has 80 (4.7%) missing values Missing
inf_im has 80 (4.7%) missing values Missing
post_im has 72 (4.2%) missing values Missing
ritm_ecg_p_01 has 152 (8.9%) missing values Missing
ritm_ecg_p_02 has 152 (8.9%) missing values Missing
ritm_ecg_p_04 has 152 (8.9%) missing values Missing
ritm_ecg_p_06 has 152 (8.9%) missing values Missing
ritm_ecg_p_07 has 152 (8.9%) missing values Missing
ritm_ecg_p_08 has 152 (8.9%) missing values Missing
n_r_ecg_p_01 has 115 (6.8%) missing values Missing
n_r_ecg_p_02 has 115 (6.8%) missing values Missing
n_r_ecg_p_03 has 115 (6.8%) missing values Missing
n_r_ecg_p_04 has 115 (6.8%) missing values Missing
n_r_ecg_p_05 has 115 (6.8%) missing values Missing
n_r_ecg_p_06 has 115 (6.8%) missing values Missing
n_r_ecg_p_08 has 115 (6.8%) missing values Missing
n_r_ecg_p_09 has 115 (6.8%) missing values Missing
n_r_ecg_p_10 has 115 (6.8%) missing values Missing
n_p_ecg_p_01 has 115 (6.8%) missing values Missing
n_p_ecg_p_03 has 115 (6.8%) missing values Missing
n_p_ecg_p_04 has 115 (6.8%) missing values Missing
n_p_ecg_p_05 has 115 (6.8%) missing values Missing
n_p_ecg_p_06 has 115 (6.8%) missing values Missing
n_p_ecg_p_07 has 115 (6.8%) missing values Missing
n_p_ecg_p_08 has 115 (6.8%) missing values Missing
n_p_ecg_p_09 has 115 (6.8%) missing values Missing
n_p_ecg_p_10 has 115 (6.8%) missing values Missing
n_p_ecg_p_11 has 115 (6.8%) missing values Missing
n_p_ecg_p_12 has 115 (6.8%) missing values Missing
GIPO_K has 369 (21.7%) missing values Missing
K_BLOOD has 371 (21.8%) missing values Missing
GIPER_NA has 375 (22.1%) missing values Missing
NA_BLOOD has 375 (22.1%) missing values Missing
ALT_BLOOD has 284 (16.7%) missing values Missing
AST_BLOOD has 285 (16.8%) missing values Missing
KFK_BLOOD has 1696 (99.8%) missing values Missing
L_BLOOD has 125 (7.4%) missing values Missing
ROE has 203 (11.9%) missing values Missing
TIME_B_S has 126 (7.4%) missing values Missing
R_AB_2_n has 108 (6.4%) missing values Missing
R_AB_3_n has 128 (7.5%) missing values Missing
NA_KB has 657 (38.6%) missing values Missing
NOT_NA_KB has 686 (40.4%) missing values Missing
LID_KB has 677 (39.8%) missing values Missing
NA_R_2_n has 108 (6.4%) missing values Missing
NA_R_3_n has 131 (7.7%) missing values Missing
NOT_NA_2_n has 110 (6.5%) missing values Missing
NOT_NA_3_n has 131 (7.7%) missing values Missing
ID is uniformly distributed Uniform
KFK_BLOOD is uniformly distributed Uniform
ID has unique values Unique
STENOK_AN has 661 (38.9%) zeros Zeros
DLIT_AG has 551 (32.4%) zeros Zeros
D_AD_ORIT has 18 (1.1%) zeros Zeros
LET_IS has 1429 (84.1%) zeros Zeros

Reproduction

Analysis started2021-06-12 20:37:29.260220
Analysis finished2021-06-12 20:38:29.490832
Duration1 minute and 0.23 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

ID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1700
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean850.5
Minimum1
Maximum1700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2021-06-12T17:38:29.570651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile85.95
Q1425.75
median850.5
Q31275.25
95-th percentile1615.05
Maximum1700
Range1699
Interquartile range (IQR)849.5

Descriptive statistics

Standard deviation490.8920452
Coefficient of variation (CV)0.5771805352
Kurtosis-1.2
Mean850.5
Median Absolute Deviation (MAD)425
Skewness0
Sum1445850
Variance240975
MonotonicityStrictly increasing
2021-06-12T17:38:29.676368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21
 
0.1%
5851
 
0.1%
5811
 
0.1%
5791
 
0.1%
5771
 
0.1%
5751
 
0.1%
5731
 
0.1%
5711
 
0.1%
5691
 
0.1%
5671
 
0.1%
Other values (1690)1690
99.4%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
17001
0.1%
16991
0.1%
16981
0.1%
16971
0.1%
16961
0.1%
16951
0.1%
16941
0.1%
16931
0.1%
16921
0.1%
16911
0.1%

AGE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct62
Distinct (%)3.7%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean61.856974
Minimum26
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2021-06-12T17:38:29.781056image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile42
Q154
median63
Q370
95-th percentile80
Maximum92
Range66
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.25993582
Coefficient of variation (CV)0.182031792
Kurtosis-0.1838397326
Mean61.856974
Median Absolute Deviation (MAD)8
Skewness-0.2197558292
Sum104662
Variance126.7861548
MonotonicityNot monotonic
2021-06-12T17:38:29.883781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6390
 
5.3%
6581
 
4.8%
6279
 
4.6%
6468
 
4.0%
7066
 
3.9%
5257
 
3.4%
6154
 
3.2%
6654
 
3.2%
5553
 
3.1%
5753
 
3.1%
Other values (52)1037
61.0%
ValueCountFrequency (%)
261
 
0.1%
272
 
0.1%
301
 
0.1%
323
 
0.2%
333
 
0.2%
346
0.4%
355
 
0.3%
362
 
0.1%
3714
0.8%
3813
0.8%
ValueCountFrequency (%)
922
 
0.1%
902
 
0.1%
885
 
0.3%
874
 
0.2%
862
 
0.1%
855
 
0.3%
847
0.4%
8316
0.9%
8213
0.8%
8112
0.7%

SEX
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
1
1065 
0
635 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
11065
62.6%
0635
37.4%

Length

2021-06-12T17:38:30.046348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:30.096215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
11065
62.6%
0635
37.4%

Most occurring characters

ValueCountFrequency (%)
11065
62.6%
0635
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11065
62.6%
0635
37.4%

Most occurring scripts

ValueCountFrequency (%)
Common1700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11065
62.6%
0635
37.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11065
62.6%
0635
37.4%

INF_ANAM
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing4
Missing (%)0.2%
Memory size13.4 KiB
0.0
1060 
1.0
410 
2.0
147 
3.0
 
79

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5088
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01060
62.4%
1.0410
 
24.1%
2.0147
 
8.6%
3.079
 
4.6%
(Missing)4
 
0.2%

Length

2021-06-12T17:38:30.227863image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:30.277728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01060
62.5%
1.0410
 
24.2%
2.0147
 
8.7%
3.079
 
4.7%

Most occurring characters

ValueCountFrequency (%)
02756
54.2%
.1696
33.3%
1410
 
8.1%
2147
 
2.9%
379
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3392
66.7%
Other Punctuation1696
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02756
81.2%
1410
 
12.1%
2147
 
4.3%
379
 
2.3%
Other Punctuation
ValueCountFrequency (%)
.1696
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5088
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02756
54.2%
.1696
33.3%
1410
 
8.1%
2147
 
2.9%
379
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII5088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02756
54.2%
.1696
33.3%
1410
 
8.1%
2147
 
2.9%
379
 
1.6%

STENOK_AN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct7
Distinct (%)0.4%
Missing106
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean2.316185696
Minimum0
Maximum6
Zeros661
Zeros (%)38.9%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2021-06-12T17:38:30.329589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.440586205
Coefficient of variation (CV)1.053709212
Kurtosis-1.447653809
Mean2.316185696
Median Absolute Deviation (MAD)1
Skewness0.4696783855
Sum3692
Variance5.956461023
MonotonicityNot monotonic
2021-06-12T17:38:30.394447image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0661
38.9%
6332
19.5%
1146
 
8.6%
2137
 
8.1%
5125
 
7.4%
3117
 
6.9%
476
 
4.5%
(Missing)106
 
6.2%
ValueCountFrequency (%)
0661
38.9%
1146
 
8.6%
2137
 
8.1%
3117
 
6.9%
476
 
4.5%
5125
 
7.4%
6332
19.5%
ValueCountFrequency (%)
6332
19.5%
5125
 
7.4%
476
 
4.5%
3117
 
6.9%
2137
 
8.1%
1146
 
8.6%
0661
38.9%

FK_STENOK
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.3%
Missing73
Missing (%)4.3%
Memory size13.4 KiB
2.0
854 
0.0
661 
3.0
 
54
1.0
 
47
4.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4881
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
2.0854
50.2%
0.0661
38.9%
3.054
 
3.2%
1.047
 
2.8%
4.011
 
0.6%
(Missing)73
 
4.3%

Length

2021-06-12T17:38:30.541024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:30.594916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0854
52.5%
0.0661
40.6%
3.054
 
3.3%
1.047
 
2.9%
4.011
 
0.7%

Most occurring characters

ValueCountFrequency (%)
02288
46.9%
.1627
33.3%
2854
 
17.5%
354
 
1.1%
147
 
1.0%
411
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3254
66.7%
Other Punctuation1627
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02288
70.3%
2854
 
26.2%
354
 
1.7%
147
 
1.4%
411
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.1627
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4881
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02288
46.9%
.1627
33.3%
2854
 
17.5%
354
 
1.1%
147
 
1.0%
411
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02288
46.9%
.1627
33.3%
2854
 
17.5%
354
 
1.1%
147
 
1.0%
411
 
0.2%

IBS_POST
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.2%
Missing51
Missing (%)3.0%
Memory size13.4 KiB
2.0
683 
1.0
548 
0.0
418 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4947
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0683
40.2%
1.0548
32.2%
0.0418
24.6%
(Missing)51
 
3.0%

Length

2021-06-12T17:38:30.738496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:30.790391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0683
41.4%
1.0548
33.2%
0.0418
25.3%

Most occurring characters

ValueCountFrequency (%)
02067
41.8%
.1649
33.3%
2683
 
13.8%
1548
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3298
66.7%
Other Punctuation1649
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02067
62.7%
2683
 
20.7%
1548
 
16.6%
Other Punctuation
ValueCountFrequency (%)
.1649
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4947
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02067
41.8%
.1649
33.3%
2683
 
13.8%
1548
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02067
41.8%
.1649
33.3%
2683
 
13.8%
1548
 
11.1%

IBS_NASL
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)2.8%
Missing1628
Missing (%)95.8%
Memory size13.4 KiB
0.0
45 
1.0
27 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters216
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.045
 
2.6%
1.027
 
1.6%
(Missing)1628
95.8%

Length

2021-06-12T17:38:30.929985image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:30.982875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.045
62.5%
1.027
37.5%

Most occurring characters

ValueCountFrequency (%)
0117
54.2%
.72
33.3%
127
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number144
66.7%
Other Punctuation72
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0117
81.2%
127
 
18.8%
Other Punctuation
ValueCountFrequency (%)
.72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0117
54.2%
.72
33.3%
127
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0117
54.2%
.72
33.3%
127
 
12.5%

GB
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing9
Missing (%)0.5%
Memory size13.4 KiB
2.0
880 
0.0
605 
3.0
195 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5073
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row0.0
3rd row2.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0880
51.8%
0.0605
35.6%
3.0195
 
11.5%
1.011
 
0.6%
(Missing)9
 
0.5%

Length

2021-06-12T17:38:31.119510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:31.170375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0880
52.0%
0.0605
35.8%
3.0195
 
11.5%
1.011
 
0.7%

Most occurring characters

ValueCountFrequency (%)
02296
45.3%
.1691
33.3%
2880
 
17.3%
3195
 
3.8%
111
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3382
66.7%
Other Punctuation1691
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02296
67.9%
2880
 
26.0%
3195
 
5.8%
111
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.1691
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5073
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02296
45.3%
.1691
33.3%
2880
 
17.3%
3195
 
3.8%
111
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5073
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02296
45.3%
.1691
33.3%
2880
 
17.3%
3195
 
3.8%
111
 
0.2%

SIM_GIPERT
Categorical

Distinct2
Distinct (%)0.1%
Missing8
Missing (%)0.5%
Memory size13.4 KiB
0.0
1635 
1.0
 
57

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5076
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01635
96.2%
1.057
 
3.4%
(Missing)8
 
0.5%

Length

2021-06-12T17:38:31.310966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:31.365819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01635
96.6%
1.057
 
3.4%

Most occurring characters

ValueCountFrequency (%)
03327
65.5%
.1692
33.3%
157
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3384
66.7%
Other Punctuation1692
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03327
98.3%
157
 
1.7%
Other Punctuation
ValueCountFrequency (%)
.1692
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5076
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03327
65.5%
.1692
33.3%
157
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03327
65.5%
.1692
33.3%
157
 
1.1%

DLIT_AG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct8
Distinct (%)0.6%
Missing248
Missing (%)14.6%
Infinite0
Infinite (%)0.0%
Mean3.340220386
Minimum0
Maximum7
Zeros551
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2021-06-12T17:38:31.410699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q37
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.098645925
Coefficient of variation (CV)0.9276770893
Kurtosis-1.821060826
Mean3.340220386
Median Absolute Deviation (MAD)3
Skewness0.06385981297
Sum4850
Variance9.601606568
MonotonicityNot monotonic
2021-06-12T17:38:31.475526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0551
32.4%
7432
25.4%
6165
 
9.7%
193
 
5.5%
573
 
4.3%
358
 
3.4%
258
 
3.4%
422
 
1.3%
(Missing)248
14.6%
ValueCountFrequency (%)
0551
32.4%
193
 
5.5%
258
 
3.4%
358
 
3.4%
422
 
1.3%
573
 
4.3%
6165
 
9.7%
7432
25.4%
ValueCountFrequency (%)
7432
25.4%
6165
 
9.7%
573
 
4.3%
422
 
1.3%
358
 
3.4%
258
 
3.4%
193
 
5.5%
0551
32.4%

ZSN_A
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.3%
Missing54
Missing (%)3.2%
Memory size13.4 KiB
0.0
1468 
1.0
 
103
3.0
 
29
2.0
 
27
4.0
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4938
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01468
86.4%
1.0103
 
6.1%
3.029
 
1.7%
2.027
 
1.6%
4.019
 
1.1%
(Missing)54
 
3.2%

Length

2021-06-12T17:38:31.640084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:31.693940image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01468
89.2%
1.0103
 
6.3%
3.029
 
1.8%
2.027
 
1.6%
4.019
 
1.2%

Most occurring characters

ValueCountFrequency (%)
03114
63.1%
.1646
33.3%
1103
 
2.1%
329
 
0.6%
227
 
0.5%
419
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3292
66.7%
Other Punctuation1646
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03114
94.6%
1103
 
3.1%
329
 
0.9%
227
 
0.8%
419
 
0.6%
Other Punctuation
ValueCountFrequency (%)
.1646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4938
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03114
63.1%
.1646
33.3%
1103
 
2.1%
329
 
0.6%
227
 
0.5%
419
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03114
63.1%
.1646
33.3%
1103
 
2.1%
329
 
0.6%
227
 
0.5%
419
 
0.4%

nr_11
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing21
Missing (%)1.2%
Memory size13.4 KiB
0.0
1637 
1.0
 
42

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5037
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01637
96.3%
1.042
 
2.5%
(Missing)21
 
1.2%

Length

2021-06-12T17:38:31.837558image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:31.886427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01637
97.5%
1.042
 
2.5%

Most occurring characters

ValueCountFrequency (%)
03316
65.8%
.1679
33.3%
142
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3358
66.7%
Other Punctuation1679
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03316
98.7%
142
 
1.3%
Other Punctuation
ValueCountFrequency (%)
.1679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5037
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03316
65.8%
.1679
33.3%
142
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03316
65.8%
.1679
33.3%
142
 
0.8%

nr_01
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing21
Missing (%)1.2%
Memory size13.4 KiB
0.0
1675 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5037
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01675
98.5%
1.04
 
0.2%
(Missing)21
 
1.2%

Length

2021-06-12T17:38:32.023060image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:32.071930image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01675
99.8%
1.04
 
0.2%

Most occurring characters

ValueCountFrequency (%)
03354
66.6%
.1679
33.3%
14
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3358
66.7%
Other Punctuation1679
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03354
99.9%
14
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5037
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03354
66.6%
.1679
33.3%
14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03354
66.6%
.1679
33.3%
14
 
0.1%

nr_02
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing21
Missing (%)1.2%
Memory size13.4 KiB
0.0
1660 
1.0
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5037
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01660
97.6%
1.019
 
1.1%
(Missing)21
 
1.2%

Length

2021-06-12T17:38:32.199620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:32.249486image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01660
98.9%
1.019
 
1.1%

Most occurring characters

ValueCountFrequency (%)
03339
66.3%
.1679
33.3%
119
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3358
66.7%
Other Punctuation1679
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03339
99.4%
119
 
0.6%
Other Punctuation
ValueCountFrequency (%)
.1679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5037
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03339
66.3%
.1679
33.3%
119
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII5037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03339
66.3%
.1679
33.3%
119
 
0.4%

nr_03
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing21
Missing (%)1.2%
Memory size13.4 KiB
0.0
1644 
1.0
 
35

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5037
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01644
96.7%
1.035
 
2.1%
(Missing)21
 
1.2%

Length

2021-06-12T17:38:32.387118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:32.437982image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01644
97.9%
1.035
 
2.1%

Most occurring characters

ValueCountFrequency (%)
03323
66.0%
.1679
33.3%
135
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3358
66.7%
Other Punctuation1679
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03323
99.0%
135
 
1.0%
Other Punctuation
ValueCountFrequency (%)
.1679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5037
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03323
66.0%
.1679
33.3%
135
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII5037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03323
66.0%
.1679
33.3%
135
 
0.7%

nr_04
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing21
Missing (%)1.2%
Memory size13.4 KiB
0.0
1650 
1.0
 
29

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5037
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01650
97.1%
1.029
 
1.7%
(Missing)21
 
1.2%

Length

2021-06-12T17:38:32.574585image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:32.623455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01650
98.3%
1.029
 
1.7%

Most occurring characters

ValueCountFrequency (%)
03329
66.1%
.1679
33.3%
129
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3358
66.7%
Other Punctuation1679
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03329
99.1%
129
 
0.9%
Other Punctuation
ValueCountFrequency (%)
.1679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5037
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03329
66.1%
.1679
33.3%
129
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII5037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03329
66.1%
.1679
33.3%
129
 
0.6%

nr_07
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing21
Missing (%)1.2%
Memory size13.4 KiB
0.0
1678 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5037
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01678
98.7%
1.01
 
0.1%
(Missing)21
 
1.2%

Length

2021-06-12T17:38:32.755102image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:32.802974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01678
99.9%
1.01
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03357
66.6%
.1679
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3358
66.7%
Other Punctuation1679
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03357
> 99.9%
11
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.1679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5037
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03357
66.6%
.1679
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03357
66.6%
.1679
33.3%
11
 
< 0.1%

nr_08
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing21
Missing (%)1.2%
Memory size13.4 KiB
0.0
1675 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5037
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01675
98.5%
1.04
 
0.2%
(Missing)21
 
1.2%

Length

2021-06-12T17:38:32.935651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:32.984523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01675
99.8%
1.04
 
0.2%

Most occurring characters

ValueCountFrequency (%)
03354
66.6%
.1679
33.3%
14
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3358
66.7%
Other Punctuation1679
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03354
99.9%
14
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5037
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03354
66.6%
.1679
33.3%
14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03354
66.6%
.1679
33.3%
14
 
0.1%

np_01
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing18
Missing (%)1.1%
Memory size13.4 KiB
0.0
1680 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5046
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01680
98.8%
1.02
 
0.1%
(Missing)18
 
1.1%

Length

2021-06-12T17:38:33.120160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:33.174032image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01680
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03362
66.6%
.1682
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3364
66.7%
Other Punctuation1682
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03362
99.9%
12
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1682
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5046
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03362
66.6%
.1682
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03362
66.6%
.1682
33.3%
12
 
< 0.1%

np_04
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing18
Missing (%)1.1%
Memory size13.4 KiB
0.0
1679 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5046
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01679
98.8%
1.03
 
0.2%
(Missing)18
 
1.1%

Length

2021-06-12T17:38:33.310617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:33.361515image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01679
99.8%
1.03
 
0.2%

Most occurring characters

ValueCountFrequency (%)
03361
66.6%
.1682
33.3%
13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3364
66.7%
Other Punctuation1682
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03361
99.9%
13
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1682
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5046
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03361
66.6%
.1682
33.3%
13
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03361
66.6%
.1682
33.3%
13
 
0.1%

np_05
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing18
Missing (%)1.1%
Memory size13.4 KiB
0.0
1671 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5046
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01671
98.3%
1.011
 
0.6%
(Missing)18
 
1.1%

Length

2021-06-12T17:38:33.494152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:33.546014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01671
99.3%
1.011
 
0.7%

Most occurring characters

ValueCountFrequency (%)
03353
66.4%
.1682
33.3%
111
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3364
66.7%
Other Punctuation1682
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03353
99.7%
111
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.1682
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5046
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03353
66.4%
.1682
33.3%
111
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03353
66.4%
.1682
33.3%
111
 
0.2%

np_07
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing18
Missing (%)1.1%
Memory size13.4 KiB
0.0
1681 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5046
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01681
98.9%
1.01
 
0.1%
(Missing)18
 
1.1%

Length

2021-06-12T17:38:33.683653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:33.732520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01681
99.9%
1.01
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03363
66.6%
.1682
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3364
66.7%
Other Punctuation1682
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03363
> 99.9%
11
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.1682
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5046
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03363
66.6%
.1682
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03363
66.6%
.1682
33.3%
11
 
< 0.1%

np_08
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing18
Missing (%)1.1%
Memory size13.4 KiB
0.0
1676 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5046
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01676
98.6%
1.06
 
0.4%
(Missing)18
 
1.1%

Length

2021-06-12T17:38:33.862168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:33.913032image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01676
99.6%
1.06
 
0.4%

Most occurring characters

ValueCountFrequency (%)
03358
66.5%
.1682
33.3%
16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3364
66.7%
Other Punctuation1682
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03358
99.8%
16
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.1682
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5046
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03358
66.5%
.1682
33.3%
16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03358
66.5%
.1682
33.3%
16
 
0.1%

np_09
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing18
Missing (%)1.1%
Memory size13.4 KiB
0.0
1680 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5046
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01680
98.8%
1.02
 
0.1%
(Missing)18
 
1.1%

Length

2021-06-12T17:38:34.047646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:34.099533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01680
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03362
66.6%
.1682
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3364
66.7%
Other Punctuation1682
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03362
99.9%
12
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1682
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5046
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03362
66.6%
.1682
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03362
66.6%
.1682
33.3%
12
 
< 0.1%

np_10
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing18
Missing (%)1.1%
Memory size13.4 KiB
0.0
1679 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5046
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01679
98.8%
1.03
 
0.2%
(Missing)18
 
1.1%

Length

2021-06-12T17:38:34.228163image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:34.280024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01679
99.8%
1.03
 
0.2%

Most occurring characters

ValueCountFrequency (%)
03361
66.6%
.1682
33.3%
13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3364
66.7%
Other Punctuation1682
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03361
99.9%
13
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1682
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5046
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03361
66.6%
.1682
33.3%
13
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03361
66.6%
.1682
33.3%
13
 
0.1%

endocr_01
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing11
Missing (%)0.6%
Memory size13.4 KiB
0.0
1461 
1.0
228 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5067
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01461
85.9%
1.0228
 
13.4%
(Missing)11
 
0.6%

Length

2021-06-12T17:38:34.412704image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:34.461539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01461
86.5%
1.0228
 
13.5%

Most occurring characters

ValueCountFrequency (%)
03150
62.2%
.1689
33.3%
1228
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3378
66.7%
Other Punctuation1689
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03150
93.3%
1228
 
6.7%
Other Punctuation
ValueCountFrequency (%)
.1689
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5067
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03150
62.2%
.1689
33.3%
1228
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII5067
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03150
62.2%
.1689
33.3%
1228
 
4.5%

endocr_02
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.6%
Memory size13.4 KiB
0.0
1648 
1.0
 
42

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5070
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01648
96.9%
1.042
 
2.5%
(Missing)10
 
0.6%

Length

2021-06-12T17:38:34.593187image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:34.643053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01648
97.5%
1.042
 
2.5%

Most occurring characters

ValueCountFrequency (%)
03338
65.8%
.1690
33.3%
142
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3380
66.7%
Other Punctuation1690
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03338
98.8%
142
 
1.2%
Other Punctuation
ValueCountFrequency (%)
.1690
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03338
65.8%
.1690
33.3%
142
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03338
65.8%
.1690
33.3%
142
 
0.8%

endocr_03
Categorical

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.6%
Memory size13.4 KiB
0.0
1677 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5070
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01677
98.6%
1.013
 
0.8%
(Missing)10
 
0.6%

Length

2021-06-12T17:38:34.778691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:34.827560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01677
99.2%
1.013
 
0.8%

Most occurring characters

ValueCountFrequency (%)
03367
66.4%
.1690
33.3%
113
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3380
66.7%
Other Punctuation1690
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03367
99.6%
113
 
0.4%
Other Punctuation
ValueCountFrequency (%)
.1690
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03367
66.4%
.1690
33.3%
113
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03367
66.4%
.1690
33.3%
113
 
0.3%

zab_leg_01
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing7
Missing (%)0.4%
Memory size13.4 KiB
0.0
1559 
1.0
 
134

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5079
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01559
91.7%
1.0134
 
7.9%
(Missing)7
 
0.4%

Length

2021-06-12T17:38:34.957213image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:35.007080image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01559
92.1%
1.0134
 
7.9%

Most occurring characters

ValueCountFrequency (%)
03252
64.0%
.1693
33.3%
1134
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3386
66.7%
Other Punctuation1693
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03252
96.0%
1134
 
4.0%
Other Punctuation
ValueCountFrequency (%)
.1693
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5079
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03252
64.0%
.1693
33.3%
1134
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII5079
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03252
64.0%
.1693
33.3%
1134
 
2.6%

zab_leg_02
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing7
Missing (%)0.4%
Memory size13.4 KiB
0.0
1572 
1.0
 
121

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5079
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01572
92.5%
1.0121
 
7.1%
(Missing)7
 
0.4%

Length

2021-06-12T17:38:35.138762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:35.187599image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01572
92.9%
1.0121
 
7.1%

Most occurring characters

ValueCountFrequency (%)
03265
64.3%
.1693
33.3%
1121
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3386
66.7%
Other Punctuation1693
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03265
96.4%
1121
 
3.6%
Other Punctuation
ValueCountFrequency (%)
.1693
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5079
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03265
64.3%
.1693
33.3%
1121
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII5079
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03265
64.3%
.1693
33.3%
1121
 
2.4%

zab_leg_03
Categorical

Distinct2
Distinct (%)0.1%
Missing7
Missing (%)0.4%
Memory size13.4 KiB
0.0
1656 
1.0
 
37

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5079
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01656
97.4%
1.037
 
2.2%
(Missing)7
 
0.4%

Length

2021-06-12T17:38:35.320244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:35.369113image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01656
97.8%
1.037
 
2.2%

Most occurring characters

ValueCountFrequency (%)
03349
65.9%
.1693
33.3%
137
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3386
66.7%
Other Punctuation1693
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03349
98.9%
137
 
1.1%
Other Punctuation
ValueCountFrequency (%)
.1693
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5079
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03349
65.9%
.1693
33.3%
137
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII5079
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03349
65.9%
.1693
33.3%
137
 
0.7%

zab_leg_04
Categorical

Distinct2
Distinct (%)0.1%
Missing7
Missing (%)0.4%
Memory size13.4 KiB
0.0
1684 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5079
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01684
99.1%
1.09
 
0.5%
(Missing)7
 
0.4%

Length

2021-06-12T17:38:35.498766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:35.547634image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01684
99.5%
1.09
 
0.5%

Most occurring characters

ValueCountFrequency (%)
03377
66.5%
.1693
33.3%
19
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3386
66.7%
Other Punctuation1693
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03377
99.7%
19
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.1693
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5079
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03377
66.5%
.1693
33.3%
19
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5079
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03377
66.5%
.1693
33.3%
19
 
0.2%

zab_leg_06
Categorical

Distinct2
Distinct (%)0.1%
Missing7
Missing (%)0.4%
Memory size13.4 KiB
0.0
1671 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5079
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01671
98.3%
1.022
 
1.3%
(Missing)7
 
0.4%

Length

2021-06-12T17:38:35.689291image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:35.740151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01671
98.7%
1.022
 
1.3%

Most occurring characters

ValueCountFrequency (%)
03364
66.2%
.1693
33.3%
122
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3386
66.7%
Other Punctuation1693
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03364
99.4%
122
 
0.6%
Other Punctuation
ValueCountFrequency (%)
.1693
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5079
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03364
66.2%
.1693
33.3%
122
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII5079
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03364
66.2%
.1693
33.3%
122
 
0.4%

S_AD_KBRIG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct30
Distinct (%)4.8%
Missing1076
Missing (%)63.3%
Infinite0
Infinite (%)0.0%
Mean136.9070513
Minimum0
Maximum260
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2021-06-12T17:38:35.792013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile80
Q1120
median140
Q3160
95-th percentile200
Maximum260
Range260
Interquartile range (IQR)40

Descriptive statistics

Standard deviation34.9978355
Coefficient of variation (CV)0.2556320889
Kurtosis0.7898039535
Mean136.9070513
Median Absolute Deviation (MAD)20
Skewness0.09046464179
Sum85430
Variance1224.84849
MonotonicityNot monotonic
2021-06-12T17:38:35.885764image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
14091
 
5.4%
13077
 
4.5%
16073
 
4.3%
12065
 
3.8%
11046
 
2.7%
15044
 
2.6%
17030
 
1.8%
18029
 
1.7%
10026
 
1.5%
8024
 
1.4%
Other values (20)119
 
7.0%
(Missing)1076
63.3%
ValueCountFrequency (%)
01
 
0.1%
403
 
0.2%
503
 
0.2%
608
 
0.5%
706
 
0.4%
8024
1.4%
9024
1.4%
10026
1.5%
1053
 
0.2%
11046
2.7%
ValueCountFrequency (%)
2602
 
0.1%
2402
 
0.1%
2301
 
0.1%
2208
 
0.5%
21010
 
0.6%
20012
 
0.7%
19013
0.8%
1851
 
0.1%
18029
1.7%
17030
1.8%

D_AD_KBRIG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21
Distinct (%)3.4%
Missing1076
Missing (%)63.3%
Infinite0
Infinite (%)0.0%
Mean81.39423077
Minimum0
Maximum190
Zeros7
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2021-06-12T17:38:35.980477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q170
median80
Q390
95-th percentile110
Maximum190
Range190
Interquartile range (IQR)20

Descriptive statistics

Standard deviation19.74504506
Coefficient of variation (CV)0.2425853144
Kurtosis4.710598727
Mean81.39423077
Median Absolute Deviation (MAD)10
Skewness-0.7458225405
Sum50790
Variance389.8668045
MonotonicityNot monotonic
2021-06-12T17:38:36.068242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
80167
 
9.8%
90154
 
9.1%
10086
 
5.1%
7078
 
4.6%
6059
 
3.5%
12016
 
0.9%
11015
 
0.9%
4011
 
0.6%
07
 
0.4%
856
 
0.4%
Other values (11)25
 
1.5%
(Missing)1076
63.3%
ValueCountFrequency (%)
07
 
0.4%
101
 
0.1%
204
 
0.2%
305
 
0.3%
4011
 
0.6%
451
 
0.1%
506
 
0.4%
6059
3.5%
652
 
0.1%
7078
4.6%
ValueCountFrequency (%)
1901
 
0.1%
1601
 
0.1%
1401
 
0.1%
12016
 
0.9%
11015
 
0.9%
10086
5.1%
951
 
0.1%
90154
9.1%
856
 
0.4%
80167
9.8%

S_AD_ORIT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct32
Distinct (%)2.2%
Missing267
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean134.5882763
Minimum0
Maximum260
Zeros8
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2021-06-12T17:38:36.164985image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile80
Q1120
median130
Q3150
95-th percentile190
Maximum260
Range260
Interquartile range (IQR)30

Descriptive statistics

Standard deviation31.34838794
Coefficient of variation (CV)0.2329206436
Kurtosis2.150612749
Mean134.5882763
Median Absolute Deviation (MAD)20
Skewness-0.283034234
Sum192865
Variance982.7214267
MonotonicityNot monotonic
2021-06-12T17:38:36.262722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
130242
14.2%
120220
12.9%
140218
12.8%
160154
9.1%
110123
7.2%
15096
 
5.6%
18068
 
4.0%
10055
 
3.2%
17054
 
3.2%
9033
 
1.9%
Other values (22)170
10.0%
(Missing)267
15.7%
ValueCountFrequency (%)
08
 
0.5%
201
 
0.1%
404
 
0.2%
503
 
0.2%
6021
 
1.2%
7012
 
0.7%
8024
1.4%
9033
1.9%
952
 
0.1%
10055
3.2%
ValueCountFrequency (%)
2601
 
0.1%
2402
 
0.1%
2302
 
0.1%
2208
 
0.5%
2106
 
0.4%
20028
1.6%
1951
 
0.1%
19025
 
1.5%
18068
4.0%
17054
3.2%

D_AD_ORIT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct20
Distinct (%)1.4%
Missing267
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean82.74947662
Minimum0
Maximum190
Zeros18
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2021-06-12T17:38:36.346500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q180
median80
Q390
95-th percentile110
Maximum190
Range190
Interquartile range (IQR)10

Descriptive statistics

Standard deviation18.32106318
Coefficient of variation (CV)0.2214039766
Kurtosis5.482849197
Mean82.74947662
Median Absolute Deviation (MAD)10
Skewness-1.028342216
Sum118580
Variance335.6613562
MonotonicityNot monotonic
2021-06-12T17:38:36.422327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
80502
29.5%
90302
17.8%
100201
11.8%
70166
 
9.8%
6084
 
4.9%
11059
 
3.5%
4028
 
1.6%
12023
 
1.4%
018
 
1.1%
5016
 
0.9%
Other values (10)34
 
2.0%
(Missing)267
15.7%
ValueCountFrequency (%)
018
 
1.1%
204
 
0.2%
301
 
0.1%
4028
 
1.6%
5016
 
0.9%
6084
 
4.9%
651
 
0.1%
70166
 
9.8%
753
 
0.2%
80502
29.5%
ValueCountFrequency (%)
1901
 
0.1%
1404
 
0.2%
1308
 
0.5%
12023
 
1.4%
11059
 
3.5%
1052
 
0.1%
100201
11.8%
956
 
0.4%
90302
17.8%
854
 
0.2%

O_L_POST
Categorical

Distinct2
Distinct (%)0.1%
Missing12
Missing (%)0.7%
Memory size13.4 KiB
0.0
1578 
1.0
 
110

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5064
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01578
92.8%
1.0110
 
6.5%
(Missing)12
 
0.7%

Length

2021-06-12T17:38:37.119465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:37.169329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01578
93.5%
1.0110
 
6.5%

Most occurring characters

ValueCountFrequency (%)
03266
64.5%
.1688
33.3%
1110
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3376
66.7%
Other Punctuation1688
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03266
96.7%
1110
 
3.3%
Other Punctuation
ValueCountFrequency (%)
.1688
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5064
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03266
64.5%
.1688
33.3%
1110
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03266
64.5%
.1688
33.3%
1110
 
2.2%

K_SH_POST
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing15
Missing (%)0.9%
Memory size13.4 KiB
0.0
1639 
1.0
 
46

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5055
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01639
96.4%
1.046
 
2.7%
(Missing)15
 
0.9%

Length

2021-06-12T17:38:37.301974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:37.351840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01639
97.3%
1.046
 
2.7%

Most occurring characters

ValueCountFrequency (%)
03324
65.8%
.1685
33.3%
146
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3370
66.7%
Other Punctuation1685
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03324
98.6%
146
 
1.4%
Other Punctuation
ValueCountFrequency (%)
.1685
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5055
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03324
65.8%
.1685
33.3%
146
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII5055
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03324
65.8%
.1685
33.3%
146
 
0.9%

MP_TP_POST
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing14
Missing (%)0.8%
Memory size13.4 KiB
0.0
1572 
1.0
 
114

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5058
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01572
92.5%
1.0114
 
6.7%
(Missing)14
 
0.8%

Length

2021-06-12T17:38:37.481496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:37.530363image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01572
93.2%
1.0114
 
6.8%

Most occurring characters

ValueCountFrequency (%)
03258
64.4%
.1686
33.3%
1114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3372
66.7%
Other Punctuation1686
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03258
96.6%
1114
 
3.4%
Other Punctuation
ValueCountFrequency (%)
.1686
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5058
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03258
64.4%
.1686
33.3%
1114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5058
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03258
64.4%
.1686
33.3%
1114
 
2.3%

SVT_POST
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing12
Missing (%)0.7%
Memory size13.4 KiB
0.0
1680 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5064
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01680
98.8%
1.08
 
0.5%
(Missing)12
 
0.7%

Length

2021-06-12T17:38:37.663974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:37.713873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01680
99.5%
1.08
 
0.5%

Most occurring characters

ValueCountFrequency (%)
03368
66.5%
.1688
33.3%
18
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3376
66.7%
Other Punctuation1688
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03368
99.8%
18
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.1688
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5064
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03368
66.5%
.1688
33.3%
18
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03368
66.5%
.1688
33.3%
18
 
0.2%

GT_POST
Categorical

Distinct2
Distinct (%)0.1%
Missing12
Missing (%)0.7%
Memory size13.4 KiB
0.0
1680 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5064
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01680
98.8%
1.08
 
0.5%
(Missing)12
 
0.7%

Length

2021-06-12T17:38:37.845515image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:37.896379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01680
99.5%
1.08
 
0.5%

Most occurring characters

ValueCountFrequency (%)
03368
66.5%
.1688
33.3%
18
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3376
66.7%
Other Punctuation1688
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03368
99.8%
18
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.1688
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5064
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03368
66.5%
.1688
33.3%
18
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03368
66.5%
.1688
33.3%
18
 
0.2%

FIB_G_POST
Categorical

Distinct2
Distinct (%)0.1%
Missing12
Missing (%)0.7%
Memory size13.4 KiB
0.0
1673 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5064
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01673
98.4%
1.015
 
0.9%
(Missing)12
 
0.7%

Length

2021-06-12T17:38:38.030022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:38.079889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01673
99.1%
1.015
 
0.9%

Most occurring characters

ValueCountFrequency (%)
03361
66.4%
.1688
33.3%
115
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3376
66.7%
Other Punctuation1688
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03361
99.6%
115
 
0.4%
Other Punctuation
ValueCountFrequency (%)
.1688
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5064
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03361
66.4%
.1688
33.3%
115
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03361
66.4%
.1688
33.3%
115
 
0.3%

ant_im
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.3%
Missing83
Missing (%)4.9%
Memory size13.4 KiB
0.0
660 
4.0
492 
1.0
392 
2.0
 
39
3.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4851
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row4.0
4th row0.0
5th row4.0

Common Values

ValueCountFrequency (%)
0.0660
38.8%
4.0492
28.9%
1.0392
23.1%
2.039
 
2.3%
3.034
 
2.0%
(Missing)83
 
4.9%

Length

2021-06-12T17:38:38.220525image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:38.275374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0660
40.8%
4.0492
30.4%
1.0392
24.2%
2.039
 
2.4%
3.034
 
2.1%

Most occurring characters

ValueCountFrequency (%)
02277
46.9%
.1617
33.3%
4492
 
10.1%
1392
 
8.1%
239
 
0.8%
334
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3234
66.7%
Other Punctuation1617
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02277
70.4%
4492
 
15.2%
1392
 
12.1%
239
 
1.2%
334
 
1.1%
Other Punctuation
ValueCountFrequency (%)
.1617
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4851
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02277
46.9%
.1617
33.3%
4492
 
10.1%
1392
 
8.1%
239
 
0.8%
334
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02277
46.9%
.1617
33.3%
4492
 
10.1%
1392
 
8.1%
239
 
0.8%
334
 
0.7%

lat_im
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.3%
Missing80
Missing (%)4.7%
Memory size13.4 KiB
1.0
838 
0.0
576 
2.0
97 
3.0
 
72
4.0
 
37

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4860
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0838
49.3%
0.0576
33.9%
2.097
 
5.7%
3.072
 
4.2%
4.037
 
2.2%
(Missing)80
 
4.7%

Length

2021-06-12T17:38:38.438902image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:38.492792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0838
51.7%
0.0576
35.6%
2.097
 
6.0%
3.072
 
4.4%
4.037
 
2.3%

Most occurring characters

ValueCountFrequency (%)
02196
45.2%
.1620
33.3%
1838
 
17.2%
297
 
2.0%
372
 
1.5%
437
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3240
66.7%
Other Punctuation1620
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02196
67.8%
1838
 
25.9%
297
 
3.0%
372
 
2.2%
437
 
1.1%
Other Punctuation
ValueCountFrequency (%)
.1620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4860
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02196
45.2%
.1620
33.3%
1838
 
17.2%
297
 
2.0%
372
 
1.5%
437
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02196
45.2%
.1620
33.3%
1838
 
17.2%
297
 
2.0%
372
 
1.5%
437
 
0.8%

inf_im
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.3%
Missing80
Missing (%)4.7%
Memory size13.4 KiB
0.0
937 
1.0
195 
2.0
191 
4.0
176 
3.0
121 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4860
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0937
55.1%
1.0195
 
11.5%
2.0191
 
11.2%
4.0176
 
10.4%
3.0121
 
7.1%
(Missing)80
 
4.7%

Length

2021-06-12T17:38:38.662310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:38.719153image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0937
57.8%
1.0195
 
12.0%
2.0191
 
11.8%
4.0176
 
10.9%
3.0121
 
7.5%

Most occurring characters

ValueCountFrequency (%)
02557
52.6%
.1620
33.3%
1195
 
4.0%
2191
 
3.9%
4176
 
3.6%
3121
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3240
66.7%
Other Punctuation1620
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02557
78.9%
1195
 
6.0%
2191
 
5.9%
4176
 
5.4%
3121
 
3.7%
Other Punctuation
ValueCountFrequency (%)
.1620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4860
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02557
52.6%
.1620
33.3%
1195
 
4.0%
2191
 
3.9%
4176
 
3.6%
3121
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02557
52.6%
.1620
33.3%
1195
 
4.0%
2191
 
3.9%
4176
 
3.6%
3121
 
2.5%

post_im
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.3%
Missing72
Missing (%)4.2%
Memory size13.4 KiB
0.0
1370 
1.0
157 
2.0
 
52
3.0
 
35
4.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4884
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01370
80.6%
1.0157
 
9.2%
2.052
 
3.1%
3.035
 
2.1%
4.014
 
0.8%
(Missing)72
 
4.2%

Length

2021-06-12T17:38:38.878726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:38.934608image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01370
84.2%
1.0157
 
9.6%
2.052
 
3.2%
3.035
 
2.1%
4.014
 
0.9%

Most occurring characters

ValueCountFrequency (%)
02998
61.4%
.1628
33.3%
1157
 
3.2%
252
 
1.1%
335
 
0.7%
414
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3256
66.7%
Other Punctuation1628
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02998
92.1%
1157
 
4.8%
252
 
1.6%
335
 
1.1%
414
 
0.4%
Other Punctuation
ValueCountFrequency (%)
.1628
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4884
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02998
61.4%
.1628
33.3%
1157
 
3.2%
252
 
1.1%
335
 
0.7%
414
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4884
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02998
61.4%
.1628
33.3%
1157
 
3.2%
252
 
1.1%
335
 
0.7%
414
 
0.3%

IM_PG_P
Categorical

Distinct2
Distinct (%)0.1%
Missing1
Missing (%)0.1%
Memory size13.4 KiB
0.0
1649 
1.0
 
50

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5097
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01649
97.0%
1.050
 
2.9%
(Missing)1
 
0.1%

Length

2021-06-12T17:38:39.091157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:39.140059image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01649
97.1%
1.050
 
2.9%

Most occurring characters

ValueCountFrequency (%)
03348
65.7%
.1699
33.3%
150
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3398
66.7%
Other Punctuation1699
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03348
98.5%
150
 
1.5%
Other Punctuation
ValueCountFrequency (%)
.1699
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5097
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03348
65.7%
.1699
33.3%
150
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5097
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03348
65.7%
.1699
33.3%
150
 
1.0%

ritm_ecg_p_01
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing152
Missing (%)8.9%
Memory size13.4 KiB
1.0
1029 
0.0
519 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4644
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.01029
60.5%
0.0519
30.5%
(Missing)152
 
8.9%

Length

2021-06-12T17:38:39.266726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:39.320544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01029
66.5%
0.0519
33.5%

Most occurring characters

ValueCountFrequency (%)
02067
44.5%
.1548
33.3%
11029
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3096
66.7%
Other Punctuation1548
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02067
66.8%
11029
33.2%
Other Punctuation
ValueCountFrequency (%)
.1548
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4644
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02067
44.5%
.1548
33.3%
11029
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02067
44.5%
.1548
33.3%
11029
22.2%

ritm_ecg_p_02
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing152
Missing (%)8.9%
Memory size13.4 KiB
0.0
1453 
1.0
 
95

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4644
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01453
85.5%
1.095
 
5.6%
(Missing)152
 
8.9%

Length

2021-06-12T17:38:39.462197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:39.511069image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01453
93.9%
1.095
 
6.1%

Most occurring characters

ValueCountFrequency (%)
03001
64.6%
.1548
33.3%
195
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3096
66.7%
Other Punctuation1548
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03001
96.9%
195
 
3.1%
Other Punctuation
ValueCountFrequency (%)
.1548
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4644
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03001
64.6%
.1548
33.3%
195
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03001
64.6%
.1548
33.3%
195
 
2.0%

ritm_ecg_p_04
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing152
Missing (%)8.9%
Memory size13.4 KiB
0.0
1525 
1.0
 
23

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4644
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01525
89.7%
1.023
 
1.4%
(Missing)152
 
8.9%

Length

2021-06-12T17:38:39.641717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:39.690586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01525
98.5%
1.023
 
1.5%

Most occurring characters

ValueCountFrequency (%)
03073
66.2%
.1548
33.3%
123
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3096
66.7%
Other Punctuation1548
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03073
99.3%
123
 
0.7%
Other Punctuation
ValueCountFrequency (%)
.1548
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4644
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03073
66.2%
.1548
33.3%
123
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03073
66.2%
.1548
33.3%
123
 
0.5%

ritm_ecg_p_06
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing152
Missing (%)8.9%
Memory size13.4 KiB
0.0
1547 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4644
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01547
91.0%
1.01
 
0.1%
(Missing)152
 
8.9%

Length

2021-06-12T17:38:39.827223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:39.877087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01547
99.9%
1.01
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03095
66.6%
.1548
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3096
66.7%
Other Punctuation1548
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03095
> 99.9%
11
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.1548
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4644
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03095
66.6%
.1548
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03095
66.6%
.1548
33.3%
11
 
< 0.1%

ritm_ecg_p_07
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing152
Missing (%)8.9%
Memory size13.4 KiB
0.0
1195 
1.0
353 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4644
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.01195
70.3%
1.0353
 
20.8%
(Missing)152
 
8.9%

Length

2021-06-12T17:38:40.005746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:40.058572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01195
77.2%
1.0353
 
22.8%

Most occurring characters

ValueCountFrequency (%)
02743
59.1%
.1548
33.3%
1353
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3096
66.7%
Other Punctuation1548
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02743
88.6%
1353
 
11.4%
Other Punctuation
ValueCountFrequency (%)
.1548
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4644
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02743
59.1%
.1548
33.3%
1353
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02743
59.1%
.1548
33.3%
1353
 
7.6%

ritm_ecg_p_08
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing152
Missing (%)8.9%
Memory size13.4 KiB
0.0
1502 
1.0
 
46

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4644
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01502
88.4%
1.046
 
2.7%
(Missing)152
 
8.9%

Length

2021-06-12T17:38:40.197201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:40.248065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01502
97.0%
1.046
 
3.0%

Most occurring characters

ValueCountFrequency (%)
03050
65.7%
.1548
33.3%
146
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3096
66.7%
Other Punctuation1548
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03050
98.5%
146
 
1.5%
Other Punctuation
ValueCountFrequency (%)
.1548
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4644
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03050
65.7%
.1548
33.3%
146
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03050
65.7%
.1548
33.3%
146
 
1.0%

n_r_ecg_p_01
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1527 
1.0
 
58

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01527
89.8%
1.058
 
3.4%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:40.380710image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:40.432571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01527
96.3%
1.058
 
3.7%

Most occurring characters

ValueCountFrequency (%)
03112
65.4%
.1585
33.3%
158
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03112
98.2%
158
 
1.8%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03112
65.4%
.1585
33.3%
158
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03112
65.4%
.1585
33.3%
158
 
1.2%

n_r_ecg_p_02
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1577 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01577
92.8%
1.08
 
0.5%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:40.567210image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:40.620069image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01577
99.5%
1.08
 
0.5%

Most occurring characters

ValueCountFrequency (%)
03162
66.5%
.1585
33.3%
18
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03162
99.7%
18
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03162
66.5%
.1585
33.3%
18
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03162
66.5%
.1585
33.3%
18
 
0.2%

n_r_ecg_p_03
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1381 
1.0
204 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01381
81.2%
1.0204
 
12.0%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:40.761690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:40.812586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01381
87.1%
1.0204
 
12.9%

Most occurring characters

ValueCountFrequency (%)
02966
62.4%
.1585
33.3%
1204
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02966
93.6%
1204
 
6.4%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02966
62.4%
.1585
33.3%
1204
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02966
62.4%
.1585
33.3%
1204
 
4.3%

n_r_ecg_p_04
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1516 
1.0
 
69

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01516
89.2%
1.069
 
4.1%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:40.945199image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:40.995092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01516
95.6%
1.069
 
4.4%

Most occurring characters

ValueCountFrequency (%)
03101
65.2%
.1585
33.3%
169
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03101
97.8%
169
 
2.2%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03101
65.2%
.1585
33.3%
169
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03101
65.2%
.1585
33.3%
169
 
1.5%

n_r_ecg_p_05
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1515 
1.0
 
70

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01515
89.1%
1.070
 
4.1%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:41.130737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:41.180570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01515
95.6%
1.070
 
4.4%

Most occurring characters

ValueCountFrequency (%)
03100
65.2%
.1585
33.3%
170
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03100
97.8%
170
 
2.2%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03100
65.2%
.1585
33.3%
170
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03100
65.2%
.1585
33.3%
170
 
1.5%

n_r_ecg_p_06
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1553 
1.0
 
32

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01553
91.4%
1.032
 
1.9%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:41.314212image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:41.363081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01553
98.0%
1.032
 
2.0%

Most occurring characters

ValueCountFrequency (%)
03138
66.0%
.1585
33.3%
132
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03138
99.0%
132
 
1.0%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03138
66.0%
.1585
33.3%
132
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03138
66.0%
.1585
33.3%
132
 
0.7%

n_r_ecg_p_08
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1581 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01581
93.0%
1.04
 
0.2%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:41.501711image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:41.554570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01581
99.7%
1.04
 
0.3%

Most occurring characters

ValueCountFrequency (%)
03166
66.6%
.1585
33.3%
14
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03166
99.9%
14
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03166
66.6%
.1585
33.3%
14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03166
66.6%
.1585
33.3%
14
 
0.1%

n_r_ecg_p_09
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1583 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01583
93.1%
1.02
 
0.1%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:41.700180image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:41.753039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01583
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03168
66.6%
.1585
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03168
99.9%
12
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03168
66.6%
.1585
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03168
66.6%
.1585
33.3%
12
 
< 0.1%

n_r_ecg_p_10
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1583 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01583
93.1%
1.02
 
0.1%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:41.886681image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:41.935550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01583
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03168
66.6%
.1585
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03168
99.9%
12
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03168
66.6%
.1585
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03168
66.6%
.1585
33.3%
12
 
< 0.1%

n_p_ecg_p_01
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1583 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01583
93.1%
1.02
 
0.1%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:42.075177image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:42.137012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01583
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03168
66.6%
.1585
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03168
99.9%
12
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03168
66.6%
.1585
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03168
66.6%
.1585
33.3%
12
 
< 0.1%

n_p_ecg_p_03
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1553 
1.0
 
32

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01553
91.4%
1.032
 
1.9%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:42.276639image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:42.328500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01553
98.0%
1.032
 
2.0%

Most occurring characters

ValueCountFrequency (%)
03138
66.0%
.1585
33.3%
132
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03138
99.0%
132
 
1.0%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03138
66.0%
.1585
33.3%
132
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03138
66.0%
.1585
33.3%
132
 
0.7%

n_p_ecg_p_04
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1580 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01580
92.9%
1.05
 
0.3%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:42.460182image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:42.517028image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01580
99.7%
1.05
 
0.3%

Most occurring characters

ValueCountFrequency (%)
03165
66.6%
.1585
33.3%
15
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03165
99.8%
15
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03165
66.6%
.1585
33.3%
15
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03165
66.6%
.1585
33.3%
15
 
0.1%

n_p_ecg_p_05
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1583 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01583
93.1%
1.02
 
0.1%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:42.657651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:42.709512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01583
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03168
66.6%
.1585
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03168
99.9%
12
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03168
66.6%
.1585
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03168
66.6%
.1585
33.3%
12
 
< 0.1%

n_p_ecg_p_06
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1558 
1.0
 
27

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01558
91.6%
1.027
 
1.6%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:42.856124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:42.910942image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01558
98.3%
1.027
 
1.7%

Most occurring characters

ValueCountFrequency (%)
03143
66.1%
.1585
33.3%
127
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03143
99.1%
127
 
0.9%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03143
66.1%
.1585
33.3%
127
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03143
66.1%
.1585
33.3%
127
 
0.6%

n_p_ecg_p_07
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1483 
1.0
 
102

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01483
87.2%
1.0102
 
6.0%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:43.045616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:43.095483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01483
93.6%
1.0102
 
6.4%

Most occurring characters

ValueCountFrequency (%)
03068
64.5%
.1585
33.3%
1102
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03068
96.8%
1102
 
3.2%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03068
64.5%
.1585
33.3%
1102
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03068
64.5%
.1585
33.3%
1102
 
2.1%

n_p_ecg_p_08
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1578 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01578
92.8%
1.07
 
0.4%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:43.246048image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:43.299903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01578
99.6%
1.07
 
0.4%

Most occurring characters

ValueCountFrequency (%)
03163
66.5%
.1585
33.3%
17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03163
99.8%
17
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03163
66.5%
.1585
33.3%
17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03163
66.5%
.1585
33.3%
17
 
0.1%

n_p_ecg_p_09
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1575 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01575
92.6%
1.010
 
0.6%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:43.435571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:43.485439image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01575
99.4%
1.010
 
0.6%

Most occurring characters

ValueCountFrequency (%)
03160
66.5%
.1585
33.3%
110
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03160
99.7%
110
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03160
66.5%
.1585
33.3%
110
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03160
66.5%
.1585
33.3%
110
 
0.2%

n_p_ecg_p_10
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1551 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01551
91.2%
1.034
 
2.0%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:43.616089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:43.664927image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01551
97.9%
1.034
 
2.1%

Most occurring characters

ValueCountFrequency (%)
03136
66.0%
.1585
33.3%
134
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03136
98.9%
134
 
1.1%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03136
66.0%
.1585
33.3%
134
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03136
66.0%
.1585
33.3%
134
 
0.7%

n_p_ecg_p_11
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1557 
1.0
 
28

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01557
91.6%
1.028
 
1.6%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:43.803555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:43.852424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01557
98.2%
1.028
 
1.8%

Most occurring characters

ValueCountFrequency (%)
03142
66.1%
.1585
33.3%
128
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03142
99.1%
128
 
0.9%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03142
66.1%
.1585
33.3%
128
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03142
66.1%
.1585
33.3%
128
 
0.6%

n_p_ecg_p_12
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing115
Missing (%)6.8%
Memory size13.4 KiB
0.0
1507 
1.0
 
78

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01507
88.6%
1.078
 
4.6%
(Missing)115
 
6.8%

Length

2021-06-12T17:38:43.981107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:44.032973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01507
95.1%
1.078
 
4.9%

Most occurring characters

ValueCountFrequency (%)
03092
65.0%
.1585
33.3%
178
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3170
66.7%
Other Punctuation1585
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03092
97.5%
178
 
2.5%
Other Punctuation
ValueCountFrequency (%)
.1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4755
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03092
65.0%
.1585
33.3%
178
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03092
65.0%
.1585
33.3%
178
 
1.6%

fibr_ter_01
Categorical

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.6%
Memory size13.4 KiB
0.0
1677 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5070
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01677
98.6%
1.013
 
0.8%
(Missing)10
 
0.6%

Length

2021-06-12T17:38:44.164615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:44.221437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01677
99.2%
1.013
 
0.8%

Most occurring characters

ValueCountFrequency (%)
03367
66.4%
.1690
33.3%
113
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3380
66.7%
Other Punctuation1690
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03367
99.6%
113
 
0.4%
Other Punctuation
ValueCountFrequency (%)
.1690
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03367
66.4%
.1690
33.3%
113
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03367
66.4%
.1690
33.3%
113
 
0.3%

fibr_ter_02
Categorical

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.6%
Memory size13.4 KiB
0.0
1674 
1.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5070
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01674
98.5%
1.016
 
0.9%
(Missing)10
 
0.6%

Length

2021-06-12T17:38:44.376025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:44.435864image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01674
99.1%
1.016
 
0.9%

Most occurring characters

ValueCountFrequency (%)
03364
66.4%
.1690
33.3%
116
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3380
66.7%
Other Punctuation1690
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03364
99.5%
116
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.1690
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03364
66.4%
.1690
33.3%
116
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03364
66.4%
.1690
33.3%
116
 
0.3%

fibr_ter_03
Categorical

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.6%
Memory size13.4 KiB
0.0
1622 
1.0
 
68

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5070
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01622
95.4%
1.068
 
4.0%
(Missing)10
 
0.6%

Length

2021-06-12T17:38:44.579480image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:44.630344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01622
96.0%
1.068
 
4.0%

Most occurring characters

ValueCountFrequency (%)
03312
65.3%
.1690
33.3%
168
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3380
66.7%
Other Punctuation1690
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03312
98.0%
168
 
2.0%
Other Punctuation
ValueCountFrequency (%)
.1690
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03312
65.3%
.1690
33.3%
168
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03312
65.3%
.1690
33.3%
168
 
1.3%

fibr_ter_05
Categorical

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.6%
Memory size13.4 KiB
0.0
1686 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5070
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01686
99.2%
1.04
 
0.2%
(Missing)10
 
0.6%

Length

2021-06-12T17:38:44.761992image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:44.814850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01686
99.8%
1.04
 
0.2%

Most occurring characters

ValueCountFrequency (%)
03376
66.6%
.1690
33.3%
14
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3380
66.7%
Other Punctuation1690
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03376
99.9%
14
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1690
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03376
66.6%
.1690
33.3%
14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03376
66.6%
.1690
33.3%
14
 
0.1%

fibr_ter_06
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.6%
Memory size13.4 KiB
0.0
1681 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5070
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01681
98.9%
1.09
 
0.5%
(Missing)10
 
0.6%

Length

2021-06-12T17:38:44.943506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:44.993374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01681
99.5%
1.09
 
0.5%

Most occurring characters

ValueCountFrequency (%)
03371
66.5%
.1690
33.3%
19
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3380
66.7%
Other Punctuation1690
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03371
99.7%
19
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.1690
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03371
66.5%
.1690
33.3%
19
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03371
66.5%
.1690
33.3%
19
 
0.2%

fibr_ter_07
Categorical

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.6%
Memory size13.4 KiB
0.0
1684 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5070
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01684
99.1%
1.06
 
0.4%
(Missing)10
 
0.6%

Length

2021-06-12T17:38:45.124025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:45.173892image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01684
99.6%
1.06
 
0.4%

Most occurring characters

ValueCountFrequency (%)
03374
66.5%
.1690
33.3%
16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3380
66.7%
Other Punctuation1690
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03374
99.8%
16
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.1690
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03374
66.5%
.1690
33.3%
16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03374
66.5%
.1690
33.3%
16
 
0.1%

fibr_ter_08
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.6%
Memory size13.4 KiB
0.0
1688 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5070
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01688
99.3%
1.02
 
0.1%
(Missing)10
 
0.6%

Length

2021-06-12T17:38:45.315513image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:45.369369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01688
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03378
66.6%
.1690
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3380
66.7%
Other Punctuation1690
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03378
99.9%
12
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1690
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03378
66.6%
.1690
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03378
66.6%
.1690
33.3%
12
 
< 0.1%

GIPO_K
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing369
Missing (%)21.7%
Memory size13.4 KiB
0.0
797 
1.0
534 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3993
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0797
46.9%
1.0534
31.4%
(Missing)369
21.7%

Length

2021-06-12T17:38:45.498024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:45.547919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0797
59.9%
1.0534
40.1%

Most occurring characters

ValueCountFrequency (%)
02128
53.3%
.1331
33.3%
1534
 
13.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2662
66.7%
Other Punctuation1331
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02128
79.9%
1534
 
20.1%
Other Punctuation
ValueCountFrequency (%)
.1331
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3993
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02128
53.3%
.1331
33.3%
1534
 
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02128
53.3%
.1331
33.3%
1534
 
13.4%

K_BLOOD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct51
Distinct (%)3.8%
Missing371
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean4.191422122
Minimum2.3
Maximum8.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2021-06-12T17:38:45.613746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile3.1
Q13.7
median4.1
Q34.6
95-th percentile5.5
Maximum8.2
Range5.9
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.754076378
Coefficient of variation (CV)0.1799094331
Kurtosis2.433997636
Mean4.191422122
Median Absolute Deviation (MAD)0.5
Skewness0.9556884439
Sum5570.4
Variance0.5686311839
MonotonicityNot monotonic
2021-06-12T17:38:45.716471image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4101
 
5.9%
3.891
 
5.4%
4.287
 
5.1%
3.979
 
4.6%
3.571
 
4.2%
4.562
 
3.6%
4.162
 
3.6%
4.360
 
3.5%
3.658
 
3.4%
3.757
 
3.4%
Other values (41)601
35.4%
(Missing)371
21.8%
ValueCountFrequency (%)
2.34
 
0.2%
2.43
 
0.2%
2.51
 
0.1%
2.74
 
0.2%
2.84
 
0.2%
2.97
 
0.4%
323
1.4%
3.130
1.8%
3.230
1.8%
3.334
2.0%
ValueCountFrequency (%)
8.21
0.1%
81
0.1%
7.81
0.1%
7.71
0.1%
7.61
0.1%
7.31
0.1%
7.21
0.1%
6.92
0.1%
6.82
0.1%
6.72
0.1%

GIPER_NA
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing375
Missing (%)22.1%
Memory size13.4 KiB
0.0
1295 
1.0
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3975
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01295
76.2%
1.030
 
1.8%
(Missing)375
 
22.1%

Length

2021-06-12T17:38:45.884023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:45.931889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01295
97.7%
1.030
 
2.3%

Most occurring characters

ValueCountFrequency (%)
02620
65.9%
.1325
33.3%
130
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2650
66.7%
Other Punctuation1325
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02620
98.9%
130
 
1.1%
Other Punctuation
ValueCountFrequency (%)
.1325
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3975
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02620
65.9%
.1325
33.3%
130
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02620
65.9%
.1325
33.3%
130
 
0.8%

NA_BLOOD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct40
Distinct (%)3.0%
Missing375
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean136.5509434
Minimum117
Maximum169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2021-06-12T17:38:45.991729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum117
5-th percentile126
Q1133
median136
Q3140
95-th percentile146
Maximum169
Range52
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.512120429
Coefficient of variation (CV)0.04769004349
Kurtosis1.203354919
Mean136.5509434
Median Absolute Deviation (MAD)4
Skewness0.122988012
Sum180930
Variance42.40771248
MonotonicityNot monotonic
2021-06-12T17:38:46.097420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
136214
12.6%
140186
10.9%
130125
 
7.4%
13381
 
4.8%
13880
 
4.7%
14363
 
3.7%
14661
 
3.6%
13457
 
3.4%
13256
 
3.3%
13948
 
2.8%
Other values (30)354
20.8%
(Missing)375
22.1%
ValueCountFrequency (%)
1176
 
0.4%
1181
 
0.1%
12011
0.6%
1215
 
0.3%
1225
 
0.3%
12315
0.9%
12410
0.6%
12513
0.8%
12610
0.6%
12724
1.4%
ValueCountFrequency (%)
1691
 
0.1%
1631
 
0.1%
1594
 
0.2%
1564
 
0.2%
1542
 
0.1%
15314
0.8%
1511
 
0.1%
15018
1.1%
1492
 
0.1%
1482
 
0.1%

ALT_BLOOD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct69
Distinct (%)4.9%
Missing284
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean0.4814548023
Minimum0.03
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2021-06-12T17:38:46.205166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.15
Q10.23
median0.38
Q30.61
95-th percentile1.28
Maximum3
Range2.97
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation0.3872611112
Coefficient of variation (CV)0.8043561086
Kurtosis6.966396053
Mean0.4814548023
Median Absolute Deviation (MAD)0.15
Skewness2.269563589
Sum681.74
Variance0.1499711683
MonotonicityNot monotonic
2021-06-12T17:38:46.302871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.15230
13.5%
0.3210
12.4%
0.45178
10.5%
0.23144
8.5%
0.38126
7.4%
0.6176
 
4.5%
0.7573
 
4.3%
0.5267
 
3.9%
0.935
 
2.1%
0.6828
 
1.6%
Other values (59)249
14.6%
(Missing)284
16.7%
ValueCountFrequency (%)
0.031
 
0.1%
0.041
 
0.1%
0.051
 
0.1%
0.0723
 
1.4%
0.081
 
0.1%
0.091
 
0.1%
0.113
 
0.2%
0.149
 
0.5%
0.15230
13.5%
0.184
 
0.2%
ValueCountFrequency (%)
31
 
0.1%
2.861
 
0.1%
2.721
 
0.1%
2.561
 
0.1%
2.42
 
0.1%
2.263
0.2%
2.121
 
0.1%
2.14
0.2%
1.965
0.3%
1.892
 
0.1%

AST_BLOOD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct58
Distinct (%)4.1%
Missing285
Missing (%)16.8%
Infinite0
Infinite (%)0.0%
Mean0.2637173145
Minimum0.04
Maximum2.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2021-06-12T17:38:46.412579image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.07
Q10.15
median0.22
Q30.33
95-th percentile0.67
Maximum2.15
Range2.11
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.201801727
Coefficient of variation (CV)0.7652198619
Kurtosis11.41787463
Mean0.2637173145
Median Absolute Deviation (MAD)0.08
Skewness2.559230591
Sum373.16
Variance0.04072393701
MonotonicityNot monotonic
2021-06-12T17:38:46.515336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.15271
15.9%
0.22169
9.9%
0.07149
8.8%
0.3145
8.5%
0.11113
 
6.6%
0.18108
 
6.4%
0.3781
 
4.8%
0.4562
 
3.6%
0.2655
 
3.2%
0.5233
 
1.9%
Other values (48)229
13.5%
(Missing)285
16.8%
ValueCountFrequency (%)
0.0417
 
1.0%
0.07149
8.8%
0.084
 
0.2%
0.12
 
0.1%
0.11113
6.6%
0.121
 
0.1%
0.131
 
0.1%
0.145
 
0.3%
0.15271
15.9%
0.18108
 
6.4%
ValueCountFrequency (%)
2.151
 
0.1%
1.751
 
0.1%
1.361
 
0.1%
1.343
0.2%
1.21
 
0.1%
1.131
 
0.1%
1.122
0.1%
1.081
 
0.1%
1.052
0.1%
1.043
0.2%

KFK_BLOOD
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct4
Distinct (%)100.0%
Missing1696
Missing (%)99.8%
Memory size13.4 KiB
1.8
1.2
3.6
1.4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row1.8
2nd row1.4
3rd row1.2
4th row3.6

Common Values

ValueCountFrequency (%)
1.81
 
0.1%
1.21
 
0.1%
3.61
 
0.1%
1.41
 
0.1%
(Missing)1696
99.8%

Length

2021-06-12T17:38:47.455787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:47.512635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.21
25.0%
1.81
25.0%
1.41
25.0%
3.61
25.0%

Most occurring characters

ValueCountFrequency (%)
.4
33.3%
13
25.0%
81
 
8.3%
41
 
8.3%
21
 
8.3%
31
 
8.3%
61
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8
66.7%
Other Punctuation4
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
13
37.5%
81
 
12.5%
41
 
12.5%
21
 
12.5%
31
 
12.5%
61
 
12.5%
Other Punctuation
ValueCountFrequency (%)
.4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.4
33.3%
13
25.0%
81
 
8.3%
41
 
8.3%
21
 
8.3%
31
 
8.3%
61
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.4
33.3%
13
25.0%
81
 
8.3%
41
 
8.3%
21
 
8.3%
31
 
8.3%
61
 
8.3%

L_BLOOD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct174
Distinct (%)11.0%
Missing125
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean8.782914286
Minimum2
Maximum27.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2021-06-12T17:38:47.587435image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.7
Q16.4
median8
Q310.45
95-th percentile15.5
Maximum27.9
Range25.9
Interquartile range (IQR)4.05

Descriptive statistics

Standard deviation3.400557014
Coefficient of variation (CV)0.3871786634
Kurtosis2.671155811
Mean8.782914286
Median Absolute Deviation (MAD)1.9
Skewness1.341661012
Sum13833.09
Variance11.56378801
MonotonicityNot monotonic
2021-06-12T17:38:47.694150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.932
 
1.9%
730
 
1.8%
830
 
1.8%
6.829
 
1.7%
7.429
 
1.7%
7.228
 
1.6%
927
 
1.6%
7.726
 
1.5%
525
 
1.5%
7.525
 
1.5%
Other values (164)1294
76.1%
(Missing)125
 
7.4%
ValueCountFrequency (%)
21
 
0.1%
2.11
 
0.1%
2.91
 
0.1%
3.22
0.1%
3.41
 
0.1%
3.51
 
0.1%
3.61
 
0.1%
3.71
 
0.1%
3.82
0.1%
3.93
0.2%
ValueCountFrequency (%)
27.91
0.1%
251
0.1%
24.91
0.1%
23.51
0.1%
23.11
0.1%
22.91
0.1%
22.61
0.1%
22.41
0.1%
22.11
0.1%
221
0.1%

ROE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct58
Distinct (%)3.9%
Missing203
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean13.44488978
Minimum1
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2021-06-12T17:38:47.799867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median10
Q318
95-th percentile35.2
Maximum140
Range139
Interquartile range (IQR)13

Descriptive statistics

Standard deviation11.29631593
Coefficient of variation (CV)0.8401940154
Kurtosis12.23825073
Mean13.44488978
Median Absolute Deviation (MAD)5
Skewness2.290275216
Sum20127
Variance127.6067536
MonotonicityNot monotonic
2021-06-12T17:38:47.906582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5134
 
7.9%
3126
 
7.4%
10104
 
6.1%
498
 
5.8%
791
 
5.4%
883
 
4.9%
679
 
4.6%
1264
 
3.8%
1563
 
3.7%
952
 
3.1%
Other values (48)603
35.5%
(Missing)203
 
11.9%
ValueCountFrequency (%)
11
 
0.1%
242
 
2.5%
3126
7.4%
498
5.8%
5134
7.9%
679
4.6%
791
5.4%
883
4.9%
952
 
3.1%
10104
6.1%
ValueCountFrequency (%)
1401
0.1%
681
0.1%
651
0.1%
611
0.1%
601
0.1%
591
0.1%
572
0.1%
552
0.1%
532
0.1%
512
0.1%

TIME_B_S
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9
Distinct (%)0.6%
Missing126
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean4.684243964
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2021-06-12T17:38:47.994347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.871043728
Coefficient of variation (CV)0.6129150722
Kurtosis-1.440660597
Mean4.684243964
Median Absolute Deviation (MAD)2
Skewness0.2602257892
Sum7373
Variance8.242892085
MonotonicityNot monotonic
2021-06-12T17:38:48.075132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2360
21.2%
9269
15.8%
1198
11.6%
3175
10.3%
6151
8.9%
7141
 
8.3%
8101
 
5.9%
592
 
5.4%
487
 
5.1%
(Missing)126
 
7.4%
ValueCountFrequency (%)
1198
11.6%
2360
21.2%
3175
10.3%
487
 
5.1%
592
 
5.4%
6151
8.9%
7141
 
8.3%
8101
 
5.9%
9269
15.8%
ValueCountFrequency (%)
9269
15.8%
8101
 
5.9%
7141
 
8.3%
6151
8.9%
592
 
5.4%
487
 
5.1%
3175
10.3%
2360
21.2%
1198
11.6%

R_AB_1_n
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing16
Missing (%)0.9%
Memory size13.4 KiB
0.0
1282 
1.0
298 
2.0
 
78
3.0
 
26

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5052
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row3.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01282
75.4%
1.0298
 
17.5%
2.078
 
4.6%
3.026
 
1.5%
(Missing)16
 
0.9%

Length

2021-06-12T17:38:48.240716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:48.293579image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01282
76.1%
1.0298
 
17.7%
2.078
 
4.6%
3.026
 
1.5%

Most occurring characters

ValueCountFrequency (%)
02966
58.7%
.1684
33.3%
1298
 
5.9%
278
 
1.5%
326
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3368
66.7%
Other Punctuation1684
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02966
88.1%
1298
 
8.8%
278
 
2.3%
326
 
0.8%
Other Punctuation
ValueCountFrequency (%)
.1684
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5052
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02966
58.7%
.1684
33.3%
1298
 
5.9%
278
 
1.5%
326
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII5052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02966
58.7%
.1684
33.3%
1298
 
5.9%
278
 
1.5%
326
 
0.5%

R_AB_2_n
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.3%
Missing108
Missing (%)6.4%
Memory size13.4 KiB
0.0
1414 
1.0
 
133
2.0
 
44
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4776
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01414
83.2%
1.0133
 
7.8%
2.044
 
2.6%
3.01
 
0.1%
(Missing)108
 
6.4%

Length

2021-06-12T17:38:48.433210image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:48.483042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01414
88.8%
1.0133
 
8.4%
2.044
 
2.8%
3.01
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03006
62.9%
.1592
33.3%
1133
 
2.8%
244
 
0.9%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3184
66.7%
Other Punctuation1592
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03006
94.4%
1133
 
4.2%
244
 
1.4%
31
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.1592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4776
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03006
62.9%
.1592
33.3%
1133
 
2.8%
244
 
0.9%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4776
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03006
62.9%
.1592
33.3%
1133
 
2.8%
244
 
0.9%
31
 
< 0.1%

R_AB_3_n
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.3%
Missing128
Missing (%)7.5%
Memory size13.4 KiB
0.0
1469 
1.0
 
86
2.0
 
15
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4716
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01469
86.4%
1.086
 
5.1%
2.015
 
0.9%
3.02
 
0.1%
(Missing)128
 
7.5%

Length

2021-06-12T17:38:48.627653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:48.684501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01469
93.4%
1.086
 
5.5%
2.015
 
1.0%
3.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03041
64.5%
.1572
33.3%
186
 
1.8%
215
 
0.3%
32
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3144
66.7%
Other Punctuation1572
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03041
96.7%
186
 
2.7%
215
 
0.5%
32
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1572
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4716
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03041
64.5%
.1572
33.3%
186
 
1.8%
215
 
0.3%
32
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03041
64.5%
.1572
33.3%
186
 
1.8%
215
 
0.3%
32
 
< 0.1%

NA_KB
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing657
Missing (%)38.6%
Memory size13.4 KiB
1.0
618 
0.0
425 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3129
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0618
36.4%
0.0425
25.0%
(Missing)657
38.6%

Length

2021-06-12T17:38:48.835133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:48.882005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0618
59.3%
0.0425
40.7%

Most occurring characters

ValueCountFrequency (%)
01468
46.9%
.1043
33.3%
1618
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2086
66.7%
Other Punctuation1043
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01468
70.4%
1618
29.6%
Other Punctuation
ValueCountFrequency (%)
.1043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3129
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01468
46.9%
.1043
33.3%
1618
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3129
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01468
46.9%
.1043
33.3%
1618
19.8%

NOT_NA_KB
Categorical

MISSING

Distinct2
Distinct (%)0.2%
Missing686
Missing (%)40.4%
Memory size13.4 KiB
1.0
701 
0.0
313 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3042
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0701
41.2%
0.0313
18.4%
(Missing)686
40.4%

Length

2021-06-12T17:38:49.015650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:49.069505image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0701
69.1%
0.0313
30.9%

Most occurring characters

ValueCountFrequency (%)
01327
43.6%
.1014
33.3%
1701
23.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2028
66.7%
Other Punctuation1014
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01327
65.4%
1701
34.6%
Other Punctuation
ValueCountFrequency (%)
.1014
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3042
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01327
43.6%
.1014
33.3%
1701
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3042
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01327
43.6%
.1014
33.3%
1701
23.0%

LID_KB
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing677
Missing (%)39.8%
Memory size13.4 KiB
0.0
627 
1.0
396 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3069
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0627
36.9%
1.0396
23.3%
(Missing)677
39.8%

Length

2021-06-12T17:38:49.192176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:49.242045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0627
61.3%
1.0396
38.7%

Most occurring characters

ValueCountFrequency (%)
01650
53.8%
.1023
33.3%
1396
 
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2046
66.7%
Other Punctuation1023
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01650
80.6%
1396
 
19.4%
Other Punctuation
ValueCountFrequency (%)
.1023
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3069
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01650
53.8%
.1023
33.3%
1396
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01650
53.8%
.1023
33.3%
1396
 
12.9%

NITR_S
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing9
Missing (%)0.5%
Memory size13.4 KiB
0.0
1496 
1.0
195 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5073
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01496
88.0%
1.0195
 
11.5%
(Missing)9
 
0.5%

Length

2021-06-12T17:38:49.368698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:49.419567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01496
88.5%
1.0195
 
11.5%

Most occurring characters

ValueCountFrequency (%)
03187
62.8%
.1691
33.3%
1195
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3382
66.7%
Other Punctuation1691
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03187
94.2%
1195
 
5.8%
Other Punctuation
ValueCountFrequency (%)
.1691
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5073
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03187
62.8%
.1691
33.3%
1195
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5073
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03187
62.8%
.1691
33.3%
1195
 
3.8%

NA_R_1_n
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing5
Missing (%)0.3%
Memory size13.4 KiB
0.0
1108 
1.0
409 
2.0
132 
3.0
 
35
4.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5085
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01108
65.2%
1.0409
 
24.1%
2.0132
 
7.8%
3.035
 
2.1%
4.011
 
0.6%
(Missing)5
 
0.3%

Length

2021-06-12T17:38:49.551184image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:49.604043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01108
65.4%
1.0409
 
24.1%
2.0132
 
7.8%
3.035
 
2.1%
4.011
 
0.6%

Most occurring characters

ValueCountFrequency (%)
02803
55.1%
.1695
33.3%
1409
 
8.0%
2132
 
2.6%
335
 
0.7%
411
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3390
66.7%
Other Punctuation1695
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02803
82.7%
1409
 
12.1%
2132
 
3.9%
335
 
1.0%
411
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.1695
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5085
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02803
55.1%
.1695
33.3%
1409
 
8.0%
2132
 
2.6%
335
 
0.7%
411
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5085
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02803
55.1%
.1695
33.3%
1409
 
8.0%
2132
 
2.6%
335
 
0.7%
411
 
0.2%

NA_R_2_n
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.3%
Missing108
Missing (%)6.4%
Memory size13.4 KiB
0.0
1474 
1.0
 
87
2.0
 
30
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4776
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01474
86.7%
1.087
 
5.1%
2.030
 
1.8%
3.01
 
0.1%
(Missing)108
 
6.4%

Length

2021-06-12T17:38:49.758664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:49.812517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01474
92.6%
1.087
 
5.5%
2.030
 
1.9%
3.01
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03066
64.2%
.1592
33.3%
187
 
1.8%
230
 
0.6%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3184
66.7%
Other Punctuation1592
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03066
96.3%
187
 
2.7%
230
 
0.9%
31
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.1592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4776
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03066
64.2%
.1592
33.3%
187
 
1.8%
230
 
0.6%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4776
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03066
64.2%
.1592
33.3%
187
 
1.8%
230
 
0.6%
31
 
< 0.1%

NA_R_3_n
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.2%
Missing131
Missing (%)7.7%
Memory size13.4 KiB
0.0
1493 
1.0
 
60
2.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4707
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01493
87.8%
1.060
 
3.5%
2.016
 
0.9%
(Missing)131
 
7.7%

Length

2021-06-12T17:38:49.962085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:50.012949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01493
95.2%
1.060
 
3.8%
2.016
 
1.0%

Most occurring characters

ValueCountFrequency (%)
03062
65.1%
.1569
33.3%
160
 
1.3%
216
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3138
66.7%
Other Punctuation1569
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03062
97.6%
160
 
1.9%
216
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.1569
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4707
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03062
65.1%
.1569
33.3%
160
 
1.3%
216
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4707
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03062
65.1%
.1569
33.3%
160
 
1.3%
216
 
0.3%

NOT_NA_1_n
Categorical

Distinct5
Distinct (%)0.3%
Missing10
Missing (%)0.6%
Memory size13.4 KiB
0.0
1237 
1.0
376 
2.0
 
53
3.0
 
17
4.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5070
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row3.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01237
72.8%
1.0376
 
22.1%
2.053
 
3.1%
3.017
 
1.0%
4.07
 
0.4%
(Missing)10
 
0.6%

Length

2021-06-12T17:38:50.153573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:50.207429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01237
73.2%
1.0376
 
22.2%
2.053
 
3.1%
3.017
 
1.0%
4.07
 
0.4%

Most occurring characters

ValueCountFrequency (%)
02927
57.7%
.1690
33.3%
1376
 
7.4%
253
 
1.0%
317
 
0.3%
47
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3380
66.7%
Other Punctuation1690
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02927
86.6%
1376
 
11.1%
253
 
1.6%
317
 
0.5%
47
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.1690
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02927
57.7%
.1690
33.3%
1376
 
7.4%
253
 
1.0%
317
 
0.3%
47
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02927
57.7%
.1690
33.3%
1376
 
7.4%
253
 
1.0%
317
 
0.3%
47
 
0.1%

NOT_NA_2_n
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.3%
Missing110
Missing (%)6.5%
Memory size13.4 KiB
0.0
1454 
1.0
 
95
2.0
 
38
3.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4770
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row2.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01454
85.5%
1.095
 
5.6%
2.038
 
2.2%
3.03
 
0.2%
(Missing)110
 
6.5%

Length

2021-06-12T17:38:50.365008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:50.416900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01454
91.4%
1.095
 
6.0%
2.038
 
2.4%
3.03
 
0.2%

Most occurring characters

ValueCountFrequency (%)
03044
63.8%
.1590
33.3%
195
 
2.0%
238
 
0.8%
33
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3180
66.7%
Other Punctuation1590
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03044
95.7%
195
 
3.0%
238
 
1.2%
33
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1590
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4770
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03044
63.8%
.1590
33.3%
195
 
2.0%
238
 
0.8%
33
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4770
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03044
63.8%
.1590
33.3%
195
 
2.0%
238
 
0.8%
33
 
0.1%

NOT_NA_3_n
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.2%
Missing131
Missing (%)7.7%
Memory size13.4 KiB
0.0
1474 
1.0
 
57
2.0
 
38

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4707
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row2.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01474
86.7%
1.057
 
3.4%
2.038
 
2.2%
(Missing)131
 
7.7%

Length

2021-06-12T17:38:50.568509image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:50.625312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01474
93.9%
1.057
 
3.6%
2.038
 
2.4%

Most occurring characters

ValueCountFrequency (%)
03043
64.6%
.1569
33.3%
157
 
1.2%
238
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3138
66.7%
Other Punctuation1569
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03043
97.0%
157
 
1.8%
238
 
1.2%
Other Punctuation
ValueCountFrequency (%)
.1569
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4707
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03043
64.6%
.1569
33.3%
157
 
1.2%
238
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4707
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03043
64.6%
.1569
33.3%
157
 
1.2%
238
 
0.8%

LID_S_n
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.6%
Memory size13.4 KiB
0.0
1211 
1.0
479 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5070
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01211
71.2%
1.0479
 
28.2%
(Missing)10
 
0.6%

Length

2021-06-12T17:38:50.764970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:50.817797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01211
71.7%
1.0479
 
28.3%

Most occurring characters

ValueCountFrequency (%)
02901
57.2%
.1690
33.3%
1479
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3380
66.7%
Other Punctuation1690
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02901
85.8%
1479
 
14.2%
Other Punctuation
ValueCountFrequency (%)
.1690
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02901
57.2%
.1690
33.3%
1479
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII5070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02901
57.2%
.1690
33.3%
1479
 
9.4%

B_BLOK_S_n
Categorical

Distinct2
Distinct (%)0.1%
Missing11
Missing (%)0.6%
Memory size13.4 KiB
0.0
1474 
1.0
215 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5067
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01474
86.7%
1.0215
 
12.6%
(Missing)11
 
0.6%

Length

2021-06-12T17:38:50.947482image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:50.998340image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01474
87.3%
1.0215
 
12.7%

Most occurring characters

ValueCountFrequency (%)
03163
62.4%
.1689
33.3%
1215
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3378
66.7%
Other Punctuation1689
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03163
93.6%
1215
 
6.4%
Other Punctuation
ValueCountFrequency (%)
.1689
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5067
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03163
62.4%
.1689
33.3%
1215
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5067
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03163
62.4%
.1689
33.3%
1215
 
4.2%

ANT_CA_S_n
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing13
Missing (%)0.8%
Memory size13.4 KiB
1.0
1125 
0.0
562 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5061
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01125
66.2%
0.0562
33.1%
(Missing)13
 
0.8%

Length

2021-06-12T17:38:51.127001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:51.177875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01125
66.7%
0.0562
33.3%

Most occurring characters

ValueCountFrequency (%)
02249
44.4%
.1687
33.3%
11125
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3374
66.7%
Other Punctuation1687
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02249
66.7%
11125
33.3%
Other Punctuation
ValueCountFrequency (%)
.1687
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02249
44.4%
.1687
33.3%
11125
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02249
44.4%
.1687
33.3%
11125
22.2%

GEPAR_S_n
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing17
Missing (%)1.0%
Memory size13.4 KiB
1.0
1203 
0.0
480 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5049
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.01203
70.8%
0.0480
 
28.2%
(Missing)17
 
1.0%

Length

2021-06-12T17:38:51.316497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:51.370350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01203
71.5%
0.0480
 
28.5%

Most occurring characters

ValueCountFrequency (%)
02163
42.8%
.1683
33.3%
11203
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3366
66.7%
Other Punctuation1683
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02163
64.3%
11203
35.7%
Other Punctuation
ValueCountFrequency (%)
.1683
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5049
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02163
42.8%
.1683
33.3%
11203
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5049
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02163
42.8%
.1683
33.3%
11203
23.8%

ASP_S_n
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing17
Missing (%)1.0%
Memory size13.4 KiB
1.0
1252 
0.0
431 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5049
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01252
73.6%
0.0431
 
25.4%
(Missing)17
 
1.0%

Length

2021-06-12T17:38:51.497020image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:51.548843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01252
74.4%
0.0431
 
25.6%

Most occurring characters

ValueCountFrequency (%)
02114
41.9%
.1683
33.3%
11252
24.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3366
66.7%
Other Punctuation1683
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02114
62.8%
11252
37.2%
Other Punctuation
ValueCountFrequency (%)
.1683
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5049
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02114
41.9%
.1683
33.3%
11252
24.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5049
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02114
41.9%
.1683
33.3%
11252
24.8%

TIKL_S_n
Categorical

Distinct2
Distinct (%)0.1%
Missing16
Missing (%)0.9%
Memory size13.4 KiB
0.0
1654 
1.0
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5052
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01654
97.3%
1.030
 
1.8%
(Missing)16
 
0.9%

Length

2021-06-12T17:38:51.689497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:51.739366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01654
98.2%
1.030
 
1.8%

Most occurring characters

ValueCountFrequency (%)
03338
66.1%
.1684
33.3%
130
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3368
66.7%
Other Punctuation1684
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03338
99.1%
130
 
0.9%
Other Punctuation
ValueCountFrequency (%)
.1684
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5052
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03338
66.1%
.1684
33.3%
130
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII5052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03338
66.1%
.1684
33.3%
130
 
0.6%

TRENT_S_n
Categorical

Distinct2
Distinct (%)0.1%
Missing16
Missing (%)0.9%
Memory size13.4 KiB
0.0
1343 
1.0
341 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5052
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.01343
79.0%
1.0341
 
20.1%
(Missing)16
 
0.9%

Length

2021-06-12T17:38:51.860040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:51.910900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01343
79.8%
1.0341
 
20.2%

Most occurring characters

ValueCountFrequency (%)
03027
59.9%
.1684
33.3%
1341
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3368
66.7%
Other Punctuation1684
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03027
89.9%
1341
 
10.1%
Other Punctuation
ValueCountFrequency (%)
.1684
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5052
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03027
59.9%
.1684
33.3%
1341
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII5052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03027
59.9%
.1684
33.3%
1341
 
6.7%

FIBR_PREDS
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1530 
1
170 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01530
90.0%
1170
 
10.0%

Length

2021-06-12T17:38:52.053526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:52.106351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
01530
90.0%
1170
 
10.0%

Most occurring characters

ValueCountFrequency (%)
01530
90.0%
1170
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01530
90.0%
1170
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common1700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01530
90.0%
1170
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01530
90.0%
1170
 
10.0%

PREDS_TAH
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1680 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01680
98.8%
120
 
1.2%

Length

2021-06-12T17:38:52.245977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:52.297839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
01680
98.8%
120
 
1.2%

Most occurring characters

ValueCountFrequency (%)
01680
98.8%
120
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01680
98.8%
120
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common1700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01680
98.8%
120
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01680
98.8%
120
 
1.2%

JELUD_TAH
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1658 
1
 
42

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01658
97.5%
142
 
2.5%

Length

2021-06-12T17:38:52.442452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:52.494346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
01658
97.5%
142
 
2.5%

Most occurring characters

ValueCountFrequency (%)
01658
97.5%
142
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01658
97.5%
142
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common1700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01658
97.5%
142
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01658
97.5%
142
 
2.5%

FIBR_JELUD
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1629 
1
 
71

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01629
95.8%
171
 
4.2%

Length

2021-06-12T17:38:52.629984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:52.685801image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
01629
95.8%
171
 
4.2%

Most occurring characters

ValueCountFrequency (%)
01629
95.8%
171
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01629
95.8%
171
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common1700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01629
95.8%
171
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01629
95.8%
171
 
4.2%

A_V_BLOK
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1643 
1
 
57

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01643
96.6%
157
 
3.4%

Length

2021-06-12T17:38:52.828450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:52.878286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
01643
96.6%
157
 
3.4%

Most occurring characters

ValueCountFrequency (%)
01643
96.6%
157
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01643
96.6%
157
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common1700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01643
96.6%
157
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01643
96.6%
157
 
3.4%

OTEK_LANC
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1541 
1
159 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01541
90.6%
1159
 
9.4%

Length

2021-06-12T17:38:53.013955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:53.062829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
01541
90.6%
1159
 
9.4%

Most occurring characters

ValueCountFrequency (%)
01541
90.6%
1159
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01541
90.6%
1159
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
Common1700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01541
90.6%
1159
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01541
90.6%
1159
 
9.4%

RAZRIV
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1646 
1
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01646
96.8%
154
 
3.2%

Length

2021-06-12T17:38:53.223363image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:53.276222image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
01646
96.8%
154
 
3.2%

Most occurring characters

ValueCountFrequency (%)
01646
96.8%
154
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01646
96.8%
154
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common1700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01646
96.8%
154
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01646
96.8%
154
 
3.2%

DRESSLER
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1625 
1
 
75

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01625
95.6%
175
 
4.4%

Length

2021-06-12T17:38:53.413855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:53.465715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
01625
95.6%
175
 
4.4%

Most occurring characters

ValueCountFrequency (%)
01625
95.6%
175
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01625
95.6%
175
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common1700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01625
95.6%
175
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01625
95.6%
175
 
4.4%

ZSN
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1306 
1
394 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
01306
76.8%
1394
 
23.2%

Length

2021-06-12T17:38:53.592378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:53.646234image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
01306
76.8%
1394
 
23.2%

Most occurring characters

ValueCountFrequency (%)
01306
76.8%
1394
 
23.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01306
76.8%
1394
 
23.2%

Most occurring scripts

ValueCountFrequency (%)
Common1700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01306
76.8%
1394
 
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01306
76.8%
1394
 
23.2%

REC_IM
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1541 
1
159 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01541
90.6%
1159
 
9.4%

Length

2021-06-12T17:38:53.787885image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:53.838749image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
01541
90.6%
1159
 
9.4%

Most occurring characters

ValueCountFrequency (%)
01541
90.6%
1159
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01541
90.6%
1159
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
Common1700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01541
90.6%
1159
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01541
90.6%
1159
 
9.4%

P_IM_STEN
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
0
1552 
1
 
148

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01552
91.3%
1148
 
8.7%

Length

2021-06-12T17:38:53.977376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-12T17:38:54.029242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
01552
91.3%
1148
 
8.7%

Most occurring characters

ValueCountFrequency (%)
01552
91.3%
1148
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01552
91.3%
1148
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Common1700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01552
91.3%
1148
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01552
91.3%
1148
 
8.7%

LET_IS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4770588235
Minimum0
Maximum7
Zeros1429
Zeros (%)84.1%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2021-06-12T17:38:54.074123image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.381817796
Coefficient of variation (CV)2.896535453
Kurtosis10.75468144
Mean0.4770588235
Median Absolute Deviation (MAD)0
Skewness3.338822871
Sum811
Variance1.90942042
MonotonicityNot monotonic
2021-06-12T17:38:54.140943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
01429
84.1%
1110
 
6.5%
354
 
3.2%
627
 
1.6%
727
 
1.6%
423
 
1.4%
218
 
1.1%
512
 
0.7%
ValueCountFrequency (%)
01429
84.1%
1110
 
6.5%
218
 
1.1%
354
 
3.2%
423
 
1.4%
512
 
0.7%
627
 
1.6%
727
 
1.6%
ValueCountFrequency (%)
727
 
1.6%
627
 
1.6%
512
 
0.7%
423
 
1.4%
354
 
3.2%
218
 
1.1%
1110
 
6.5%
01429
84.1%

Interactions

2021-06-12T17:37:54.973633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:55.099294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:55.197067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:55.286826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:55.379544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:55.470725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:55.566464image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:55.658192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:55.756927image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:55.848681image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:55.942430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:56.037177image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:56.126937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:56.209750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:56.299507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:56.390270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:56.479028image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:56.572746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:56.660510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:56.749273image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:56.839052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:56.929821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:57.019550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:57.104323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:57.192119image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:57.272903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:57.356681image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:57.537195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:57.623964image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:57.704751image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:57.791518image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:57.883240image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:57.974035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:58.065783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:58.155512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:58.246270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:58.331074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:58.411860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:58.509596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:58.597330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:58.703048image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:58.790814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:58.884563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:58.983298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:59.068103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:59.146892image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:59.234654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:59.326382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:59.414177image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:59.500945image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:59.588710image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:59.676446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:59.768233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:59.850006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:37:59.936784image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:00.029533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:00.127239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:00.217030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:00.304795image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:00.393560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:00.601971image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:00.693750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:00.793459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:00.895186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:00.990930image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:01.081686image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:01.170484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:01.254256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:01.336007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:01.418816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:01.501595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:01.582373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:01.665160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:01.741921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:01.818748image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:01.899530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:01.984305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:02.060069image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:02.145875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:02.231644image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:02.315388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:02.406176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:02.494940image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:02.585665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:02.682405image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:02.770205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:02.862953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:02.949690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:03.040448image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:03.126218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:03.209023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:03.302780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:03.396529image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:03.485278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:03.582001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:03.673755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:03.766506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:03.853274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:03.940043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:04.027808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:04.112582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:04.335021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:04.423781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:04.502571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:04.588339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:04.666131image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:04.745918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:04.825705image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:04.901504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:04.979263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:05.064069image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:05.147843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:05.229619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:05.322376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:05.411139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:05.495912image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:05.586640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:05.673406image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:05.765192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:05.847973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:05.936733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:06.024498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:06.111269image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:06.202026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:06.285800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:06.367581image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:06.455346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:06.545109image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:06.631874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:06.719636image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:06.803418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:06.884199image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:06.965981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:07.043773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:07.124560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:07.203346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:07.293075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:07.372893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:07.451684image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:07.531471image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:07.613220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:07.699987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:07.784797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:07.870562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:07.952345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:08.036120image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:08.116905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:08.199681image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:08.281465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:08.354268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:08.435053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:08.514838image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:08.775144image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:08.862907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:08.946686image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:09.032423image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:09.116239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:09.194025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:09.277798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:09.362572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:09.444357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:09.530123image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:09.628829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:09.713633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:09.798407image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:09.877198image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:09.961968image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:10.048736image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:10.134507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:10.219281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:10.305051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:10.389824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:10.475597image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:10.555386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:10.641147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:10.728919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:10.813693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:10.891482image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:10.977234image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:11.058039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:11.135831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:11.220605image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:11.310359image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:11.396102image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:11.476917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:11.553711image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:11.643441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:11.727248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:11.805041image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:11.881835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:11.962620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:12.046364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:12.127148image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:12.209927image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:12.286754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:12.363514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:12.440344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:12.519130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:12.601908image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:12.678703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:12.758489image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:12.836281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:12.915072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:12.995855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:13.075611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:13.154437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:13.237209image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:13.318989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:13.407722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:13.506459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:13.607188image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:13.711909image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:13.802701image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:13.888469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:13.979193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:14.288368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:14.390096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:14.482848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:14.574635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:14.663364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:14.752126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:14.834939image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:14.921685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:15.023402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:15.120173image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:15.213923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:15.308671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:15.397433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:15.496166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:15.593875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:15.693618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:15.782373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:15.884101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:15.981839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:16.071631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:16.165378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:16.250154image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:16.333931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:16.427679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:16.523424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:16.620130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:16.708921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:16.797668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:16.885455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:17.005102image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:17.100847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:17.194626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:17.290372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:17.381097image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:17.468895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:17.559619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:17.647415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:17.733156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:17.825910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:17.915697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-12T17:38:18.014402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-06-12T17:38:54.423481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-12T17:38:56.098998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-12T17:38:57.780548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-12T17:38:59.473973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-06-12T17:38:18.570956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-12T17:38:22.935273image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-12T17:38:25.678979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-06-12T17:38:28.980197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

IDAGESEXINF_ANAMSTENOK_ANFK_STENOKIBS_POSTIBS_NASLGBSIM_GIPERTDLIT_AGZSN_Anr_11nr_01nr_02nr_03nr_04nr_07nr_08np_01np_04np_05np_07np_08np_09np_10endocr_01endocr_02endocr_03zab_leg_01zab_leg_02zab_leg_03zab_leg_04zab_leg_06S_AD_KBRIGD_AD_KBRIGS_AD_ORITD_AD_ORITO_L_POSTK_SH_POSTMP_TP_POSTSVT_POSTGT_POSTFIB_G_POSTant_imlat_iminf_impost_imIM_PG_Pritm_ecg_p_01ritm_ecg_p_02ritm_ecg_p_04ritm_ecg_p_06ritm_ecg_p_07ritm_ecg_p_08n_r_ecg_p_01n_r_ecg_p_02n_r_ecg_p_03n_r_ecg_p_04n_r_ecg_p_05n_r_ecg_p_06n_r_ecg_p_08n_r_ecg_p_09n_r_ecg_p_10n_p_ecg_p_01n_p_ecg_p_03n_p_ecg_p_04n_p_ecg_p_05n_p_ecg_p_06n_p_ecg_p_07n_p_ecg_p_08n_p_ecg_p_09n_p_ecg_p_10n_p_ecg_p_11n_p_ecg_p_12fibr_ter_01fibr_ter_02fibr_ter_03fibr_ter_05fibr_ter_06fibr_ter_07fibr_ter_08GIPO_KK_BLOODGIPER_NANA_BLOODALT_BLOODAST_BLOODKFK_BLOODL_BLOODROETIME_B_SR_AB_1_nR_AB_2_nR_AB_3_nNA_KBNOT_NA_KBLID_KBNITR_SNA_R_1_nNA_R_2_nNA_R_3_nNOT_NA_1_nNOT_NA_2_nNOT_NA_3_nLID_S_nB_BLOK_S_nANT_CA_S_nGEPAR_S_nASP_S_nTIKL_S_nTRENT_S_nFIBR_PREDSPREDS_TAHJELUD_TAHFIBR_JELUDA_V_BLOKOTEK_LANCRAZRIVDRESSLERZSNREC_IMP_IM_STENLET_IS
0177.012.01.01.02.0NaN3.00.07.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaN180.0100.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.04.70.0138.0NaNNaNNaN8.016.04.00.00.01.0NaNNaNNaN0.00.00.00.00.00.00.01.00.00.01.01.00.00.0000000000000
1255.011.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaN120.090.00.00.00.00.00.00.04.01.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.03.50.0132.00.380.18NaN7.83.02.00.00.00.01.00.01.00.00.00.00.01.00.00.01.00.01.01.01.00.01.0000000000000
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